14.2.12 Multiple Kernel Learning, MKL

Devroye, L., and Machell, F.,
Data Structures in Kernel Density Estimation,
PAMI(7), No. 3, May 1985, pp. 360-366. BibRef 8505

Wu, M., Schölkopf, B., and Bakir, G.,
A Direct Method for Building Sparse Kernel Learning Algorithms,
MachLearnRes(7), No. 4, 2006, pp. 603-624.
WWW Link. BibRef 0600

Wu, M., and Schölkopf, B.,
A Local Learning Approach for Clustering,
NIPS06(1529-1536).
WWW Link. BibRef 0600

Wang, Z.[Zhe], Chen, S.C.[Song-Can], Sun, T.[Tingkai],
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm,
PAMI(30), No. 2, February 2008, pp. 348-353.
IEEE DOI 0712
BibRef

Tan, X.Y.[Xiao-Yang], Chen, S.C.[Song-Can], Li, J.[Jun], Zhou, Z.H.[Zhi-Hua],
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion,
CVPR06(I: 138-145).
IEEE DOI 0606
BibRef

El-Yaniv, R.[Ran], Pechyony, D.[Dmitry], Yom-Tov, E.[Elad],
Better multiclass classification via a margin-optimized single binary problem,
PRL(29), No. 14, October 2008, pp. 1954-1959.
Elsevier DOI 0804
Multiclass classification; Support vector machines; Multiple kernel learning BibRef

Filippone, M.[Maurizio], Camastra, F.[Francesco], Masulli, F.[Francesco], Rovetta, S.[Stefano],
A survey of kernel and spectral methods for clustering,
PR(41), No. 1, January 2008, pp. 176-190.
Elsevier DOI 0710
Award, Pattern Recognition. Survey, Clustering. Partitional clustering; Mercer kernels; Kernel clustering; Kernel fuzzy clustering; Spectral clustering BibRef

Gonen, M.[Mehmet], Alpaydin, E.[Ethem],
Cost-conscious multiple kernel learning,
PRL(31), No. 9, 1 July 2010, pp. 959-965.
Elsevier DOI 1004
Support vector machines; Kernel combination; Multiple kernel learning BibRef

Zeng, H.[Hong], Cheung, Y.M.[Yiu-Ming],
A new feature selection method for Gaussian mixture clustering,
PR(42), No. 2, February 2009, pp. 243-250.
Elsevier DOI 0810
Gaussian mixture; Clustering; Feature selection; Relevance; Redundance BibRef

Zeng, H.[Hong], Cheung, Y.M.[Yiu-Ming],
Feature Selection and Kernel Learning for Local Learning-Based Clustering,
PAMI(33), No. 8, August 2011, pp. 1532-1547.
IEEE DOI 1107

See also Direct Method for Building Sparse Kernel Learning Algorithms, A. LLC Wu and Scholkopf? BibRef

Jia, H.[Hong], Cheung, Y.M.[Yiu-Ming], Liu, J.M.[Ji-Ming],
Cooperative and penalized competitive learning with application to kernel-based clustering,
PR(47), No. 9, 2014, pp. 3060-3069.
Elsevier DOI 1406
Competitive learning BibRef

Ulas, A.[Aydin], Yildiz, O.T.[Olcay Taner], Alpaydin, E.[Ethem],
Cost-conscious comparison of supervised learning algorithms over multiple data sets,
PR(45), No. 4, 2012, pp. 1772-1781.
Elsevier DOI 1410
Machine learning BibRef

Gonen, M.[Mehmet], Alpaydin, E.[Ethem],
Regularizing multiple kernel learning using response surface methodology,
PR(44), No. 1, January 2011, pp. 159-171.
Elsevier DOI 1011
Support vector machine; Multiple kernel learning; Regularization; Response surface methodology BibRef

Gönen, M.[Mehmet], Alpaydin, E.[Ethem],
Localized algorithms for multiple kernel learning,
PR(46), No. 3, March 2013, pp. 795-807.
Elsevier DOI 1212
BibRef
Earlier:
Localized Multiple Kernel Regression,
ICPR10(1425-1428).
IEEE DOI 1008
Multiple kernel learning; Support vector machines; Support vector regression; Classification; Regression; Selective attention BibRef

Liang, Z.Z.[Zhi-Zheng], Li, Y.F.[You-Fu],
Multiple kernels for generalised discriminant analysis,
IET-CV(4), No. 2, June 2010, pp. 117-128.
DOI Link 1007
BibRef

Yger, F., Rakotomamonjy, A.,
Wavelet kernel learning,
PR(44), No. 10-11, October-November 2011, pp. 2614-2629.
Elsevier DOI 1101
Wavelet; Multiple kernel learning; SVM; Quadratic mirror filter BibRef

Lin, Y.Y.[Yen-Yu], Liu, T.L.[Tyng-Luh], Fuh, C.S.[Chiou-Shann],
Multiple Kernel Learning for Dimensionality Reduction,
PAMI(33), No. 6, June 2011, pp. 1147-1160.
IEEE DOI 1105
Multiple descriptors. Transform into unified space. BibRef

Wang, S.H.[Shu-Hui], Huang, Q.M.[Qing-Ming], Jiang, S.Q.[Shu-Qiang], Tian, Q.[Qi],
S3-MKL: Scalable Semi-Supervised Multiple Kernel Learning for Real-World Image Applications,
MultMed(14), No. 4, 2012, pp. 1259-1274.
IEEE DOI 1208
BibRef
Earlier: A1, A3, A2, A4:
Multiple Kernel Learning with High Order Kernels,
ICPR10(2138-2141).
IEEE DOI 1008
BibRef

Han, Y., Yang, K., Liu, G.,
L_p Norm Localized Multiple Kernel Learning via Semi-Definite Programming,
SPLetters(19), No. 10, October 2012, pp. 688-691.
IEEE DOI 1209
BibRef

Yang, J.J.[Jing-Jing], Tian, Y.H.[Yong-Hong], Duan, L.Y.[Ling-Yu], Huang, T., Gao, W.[Wen],
Group-Sensitive Multiple Kernel Learning for Object Recognition,
IP(21), No. 5, May 2012, pp. 2838-2852.
IEEE DOI 1204
BibRef

Yang, J.J.[Jing-Jing], Li, Y.N.[Yuan-Ning], Tian, Y.H.[Yong-Hong], Duan, L.Y.[Ling-Yu], Gao, W.[Wen],
Group-sensitive multiple kernel learning for object categorization,
ICCV09(436-443).
IEEE DOI 0909
BibRef

Gu, Y.F.[Yan-Feng], Wang, C.[Chen], You, D.[Di], Zhang, Y.H.[Yu-Hang], Wang, S.Z.[Shi-Zhe], Zhang, Y.[Ye],
Representative Multiple Kernel Learning for Classification in Hyperspectral Imagery,
GeoRS(50), No. 7, July 2012, pp. 2852-2865.
IEEE DOI 1208
BibRef

Gu, Y.F.[Yan-Feng], Wang, S.Z.[Shi-Zhe], Jia, X.,
Spectral Unmixing in Multiple-Kernel Hilbert Space for Hyperspectral Imagery,
GeoRS(51), No. 7, 2013, pp. 3968-3981.
IEEE DOI Hilbert space; spectral unmixing; support vector machines (SVMs) 1307
BibRef

Guo, Z.Y.[Zhen-Yu], Wang, Z.J.,
Cross-Domain Object Recognition Via Input-Output Kernel Analysis,
IP(22), No. 8, 2013, pp. 3108-3119.
IEEE DOI 1307
cross-domain learning; vector-valued functions; Multiple Kernel Learning BibRef

Shang, F.H.[Fan-Hua], Jiao, L.C., Liu, Y.Y.[Yuan-Yuan], Tong, H.H.[Hang-Hang],
Semi-supervised learning with nuclear norm regularization,
PR(46), No. 8, August 2013, pp. 2323-2336.
Elsevier DOI 1304
Semi-supervised learning (SSL); Low-rank kernel learning; Graph Laplacian; Nuclear norm regularization; Pairwise constraints BibRef

Baghshah, M.S.[M. Soleymani], Afsari, F., Shouraki, S.B.[S. Bagheri], Eslami, E.,
Scalable semi-supervised clustering by spectral kernel learning,
PRL(45), No. 1, 2014, pp. 161-171.
Elsevier DOI 1407
Kernel learning BibRef

Baghshah, M.S.[Mahdieh Soleymani], Shouraki, S.B.[Saeed Bagheri],
Efficient Kernel Learning from Constraints and Unlabeled Data,
ICPR10(3364-3367).
IEEE DOI 1008
BibRef

Bucak, S.S.[Serhat Selcuk], Jin, R.[Rong], Jain, A.K.[Anil K.],
Multiple Kernel Learning for Visual Object Recognition: A Review,
PAMI(36), No. 7, July 2014, pp. 1354-1369.
IEEE DOI 1407
BibRef
Earlier:
Multi-label learning with incomplete class assignments,
CVPR11(2801-2808).
IEEE DOI 1106
Histograms BibRef

Han, Y.[Yina], Liu, G.Z.[Gui-Zhong],
Biologically inspired task oriented gist model for scene classification,
CVIU(117), No. 1, January 2013, pp. 76-95.
Elsevier DOI 1212
Scene classification; Scene gist; Localized multiple kernel learning; SVM BibRef

Lu, Y.T.[Yan-Ting], Wang, L.T.[Lian-Tao], Lu, J.F.[Jian-Feng], Yang, J.Y.[Jing-Yu], Shen, C.H.[Chun-Hua],
Multiple kernel clustering based on centered kernel alignment,
PR(47), No. 11, 2014, pp. 3656-3664.
Elsevier DOI 1407
Clustering BibRef

Lu, Y.T.[Yan-Ting], Lu, J.F.[Jian-Feng], Yang, J.Y.[Jing-Yu],
Adaptive kernel learning based on centered alignment for hierarchical classification,
ICPR12(569-572).
WWW Link. 1302
BibRef

Thiagarajan, J.J., Ramamurthy, K.N., Spanias, A.,
Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning,
IP(23), No. 7, July 2014, pp. 2905-2915.
IEEE DOI 1407
Clustering algorithms BibRef

Song, H., Thiagarajan, J.J., Ramamurthy, K.N., Spanias, A.,
Auto-context modeling using multiple Kernel learning,
ICIP16(1868-1872)
IEEE DOI 1610
Computational modeling BibRef

Tsai, J.T.[Jeng-Tsung], Lin, Y.Y.[Yen-Yu], Liao, H.Y.M.,
Per-Cluster Ensemble Kernel Learning for Multi-Modal Image Clustering With Group-Dependent Feature Selection,
MultMed(16), No. 8, December 2014, pp. 2229-2241.
IEEE DOI 1502
feature selection BibRef

Liu, K.H.[Keng-Hao], Lin, Y.Y.[Yen-Yu], Chen, C.S.[Chu-Song],
Linear Spectral Mixture Analysis via Multiple-Kernel Learning for Hyperspectral Image Classification,
GeoRS(53), No. 4, April 2015, pp. 2254-2269.
IEEE DOI 1502
hyperspectral imaging BibRef

Rossi, L.[Luca], Torsello, A.[Andrea], Hancock, E.R.[Edwin R.],
Unfolding Kernel embeddings of graphs: Enhancing class separation through manifold learning,
PR(48), No. 11, 2015, pp. 3357-3370.
Elsevier DOI 1506
Kernel learning BibRef

Bai, L.[Lu], Cui, L.X.[Li-Xin], Rossi, L.[Luca], Xu, L.X.[Li-Xiang], Bai, X.[Xiao], Hancock, E.R.[Edwin R.],
Local-global nested graph kernels using nested complexity traces,
PRL(134), 2020, pp. 87-95.
Elsevier DOI 2005
BibRef
Earlier: A1, A3, A2, A5, Only:
A Nested Alignment Graph Kernel Through the Dynamic Time Warping Framework,
GbRPR17(59-69).
Springer DOI 1706
Graph kernels, Depth-based complexity traces, Nested kernels BibRef

Torsello, A.[Andrea], Gasparetto, A.[Andrea], Rossi, L.[Luca], Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Transitive State Alignment for the Quantum Jensen-Shannon Kernel,
SSSPR14(22-31).
Springer DOI 1408
Kernel methods. BibRef

Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.,
Multiple Spectral-Spatial Classification Approach for Hyperspectral Data,
GeoRS(48), No. 11, November 2010, pp. 4122-4132.
IEEE DOI 1011

See also Segmentation and classification of hyperspectral images using watershed transformation. BibRef

Liu, T., Gu, Y., Jia, X., Benediktsson, J.A., Chanussot, J.,
Class-Specific Sparse Multiple Kernel Learning for Spectral-Spatial Hyperspectral Image Classification,
GeoRS(54), No. 12, December 2016, pp. 7351-7365.
IEEE DOI 1612
hyperspectral imaging
See also Spectral-Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation. BibRef

Gu, Y., Chanussot, J., Jia, X., Benediktsson, J.A.,
Multiple Kernel Learning for Hyperspectral Image Classification: A Review,
GeoRS(55), No. 11, November 2017, pp. 6547-6565.
IEEE DOI 1711
Data structures, Kernel, Neural networks, Support vector machines, Classification, heterogeneous features, hyperspectral images (HSIs), multiple kernel learning (MKL), remote sensing BibRef

Liu, T., Gu, Y., Chanussot, J., Dalla Mura, M.,
Multimorphological Superpixel Model for Hyperspectral Image Classification,
GeoRS(55), No. 12, December 2017, pp. 6950-6963.
IEEE DOI 1712
Data mining, Feature extraction, Hyperspectral imaging, Kernel, Shape, Spatial resolution, Hyperspectral images (HSIs), superpixel BibRef

Aptoula, E., Dalla Mura, M., LefŽèvre, S.,
Vector Attribute Profiles for Hyperspectral Image Classification,
GeoRS(54), No. 6, June 2016, pp. 3208-3220.
IEEE DOI 1606
hyperspectral imaging BibRef

Xia, J.S.[Jun-Shi], Chanussot, J., Du, P.J.[Pei-Jun], He, X.[Xiyan],
Spectral-Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields,
GeoRS(53), No. 5, May 2015, pp. 2532-2546.
IEEE DOI 1502
Markov processes BibRef

Xia, J.S.[Jun-Shi], Chanussot, J., Du, P.J.[Pei-Jun], He, X.[Xiyan],
Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples,
GeoRS(54), No. 3, March 2016, pp. 1519-1531.
IEEE DOI 1603
Accuracy BibRef

Xia, J.S.[Jun-Shi], Dalla Mura, M., Chanussot, J., Du, P.J.[Pei-Jun], He, X.[Xiyan],
Random Subspace Ensembles for Hyperspectral Image Classification With Extended Morphological Attribute Profiles,
GeoRS(53), No. 9, September 2015, pp. 4768-4786.
IEEE DOI 1506
Feature extraction BibRef

Gu, Y., Liu, T., Jia, X., Benediktsson, J.A., Chanussot, J.,
Nonlinear Multiple Kernel Learning With Multiple-Structure-Element Extended Morphological Profiles for Hyperspectral Image Classification,
GeoRS(54), No. 6, June 2016, pp. 3235-3247.
IEEE DOI 1606
geophysical image processing BibRef

Wang, Q., Gu, Y., Tuia, D.,
Discriminative Multiple Kernel Learning for Hyperspectral Image Classification,
GeoRS(54), No. 7, July 2016, pp. 3912-3927.
IEEE DOI 1606
Hilbert space BibRef

Wang, Q.F.[Qi-Fan], Si, L.[Luo], Zhang, D.[Dan],
A Discriminative Data-Dependent Mixture-Model Approach for Multiple Instance Learning in Image Classification,
ECCV12(IV: 660-673).
Springer DOI 1210
BibRef

Zhu, C.M.[Chang-Ming], Gao, D.[Daqi],
Improved multi-kernel classification machine with Nyström approximation technique,
PR(48), No. 4, 2015, pp. 1490-1509.
Elsevier DOI 1502
Multiple kernel learning BibRef

Nazarpour, A.[Abdollah], Adibi, P.[Peyman],
Two-stage multiple kernel learning for supervised dimensionality reduction,
PR(48), No. 5, 2015, pp. 1854-1862.
Elsevier DOI 1502
Supervised dimensionality reduction BibRef

Yuan, Y.[Ying], Lu, W.M.[Wei-Ming], Wu, F.[Fei], Zhuang, Y.T.[Yue-Ting],
Multiple kernel learning with NOn-conVex group spArsity,
JVCIR(25), No. 7, 2014, pp. 1616-1624.
Elsevier DOI 1410
Multiple kernel learning BibRef

Gevaert, C.M.[Caroline M.], Persello, C.[Claudio], Vosselman, G.[George],
Optimizing Multiple Kernel Learning for the Classification of UAV Data,
RS(8), No. 12, 2016, pp. 1025.
DOI Link 1612
BibRef

Cui, Y.W.[Yan-Wei], Chapel, L.[Laetitia], Lefèvre, S.[Sébastien],
Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef
Earlier:
A Subpath Kernel for Learning Hierarchical Image Representations,
GbRPR15(34-43).
Springer DOI 1511
BibRef

Wang, Y.Q.[Yue-Qing], Liu, X.W.[Xin-Wang], Dou, Y.[Yong], Lv, Q.[Qi], Lu, Y.[Yao],
Multiple kernel learning with hybrid kernel alignment maximization,
PR(70), No. 1, 2017, pp. 104-111.
Elsevier DOI 1706
Multiple kernel learning BibRef

Liu, X.W.[Xin-Wang],
Incomplete Multiple Kernel Alignment Maximization for Clustering,
PAMI(46), No. 3, March 2024, pp. 1412-1424.
IEEE DOI Code:
WWW Link. 2402
Kernel, Clustering algorithms, Optimization, Task analysis, Pattern analysis, Partitioning algorithms, Minimization, kernel alignment maximization BibRef

Zhang, T.J.[Tie-Jian], Liu, X.W.[Xin-Wang], Gong, L.[Lei], Wang, S.W.[Si-Wei], Niu, X.[Xin], Shen, L.[Li],
Late Fusion Multiple Kernel Clustering With Local Kernel Alignment Maximization,
MultMed(25), 2023, pp. 993-1007.
IEEE DOI 2303
Kernel, Clustering algorithms, Robustness, Partitioning algorithms, Optimization, Manifolds, Benchmark testing, block diagonal structure BibRef

Liu, X.W.[Xin-Wang],
Hyperparameter-Free Localized Simple Multiple Kernel K-means With Global Optimum,
PAMI(45), No. 7, July 2023, pp. 8566-8576.
IEEE DOI 2306
Kernel, Optimization, Clustering algorithms, Task analysis, Partitioning algorithms, Minimization, Standards, clustering ensemble BibRef

Liu, X.W.[Xin-Wang], Zhou, S.H.[Si-Hang], Liu, L.[Li], Tang, C.[Chang], Wang, S.W.[Si-Wei], Liu, J.Y.[Ji-Yuan], Zhang, Y.[Yi],
Localized Simple Multiple Kernel K-means,
ICCV21(9273-9281)
IEEE DOI 2203
Codes, Force, Clustering algorithms, Benchmark testing, Boosting, Kernel, Transfer/Low-shot/Semi/Unsupervised Learning, Optimization and learning methods BibRef

Jiao, Y.L.[Yun-Long], Vert, J.P.[Jean-Philippe],
The Kendall and Mallows Kernels for Permutations,
PAMI(40), No. 7, July 2018, pp. 1755-1769.
IEEE DOI 1806
Analytical models, Bioinformatics, Correlation, Data models, Gene expression, Kernel, Sorting, Kendall tau correlation, supervised classification of biomedical data BibRef

Sedghi, M., Atia, G., Georgiopoulos, M.,
Robust Manifold Learning via Conformity Pursuit,
SPLetters(26), No. 3, March 2019, pp. 425-429.
IEEE DOI 1903
data analysis, data structures, learning (artificial intelligence), matrix multiplication, reproducing kernel BibRef

Liu, T., Zhang, X., Gu, Y.,
Unsupervised Cross-Temporal Classification of Hyperspectral Images With Multiple Geodesic Flow Kernel Learning,
GeoRS(57), No. 12, December 2019, pp. 9688-9701.
IEEE DOI 1912
Kernel, Manifolds, Hyperspectral imaging, Task analysis, Support vector machines, Geodesic flow kernel (GFK), unsupervised classification BibRef

Wang, Z.[Zhe], Zhu, Z.H.[Zong-Hai], Li, D.D.[Dong-Dong],
Collaborative and geometric multi-kernel learning for multi-class classification,
PR(99), 2020, pp. 107050.
Elsevier DOI 1912
Multi-class classification, Empirical kernel mapping, Multiple empirical kernel learning, Regularized learning BibRef

Liu, X.W.[Xin-Wang], Zhu, X.Z.[Xin-Zhong], Li, M.M.[Miao-Miao], Wang, L.[Lei], Zhu, E.[En], Liu, T.L.[Tong-Liang], Kloft, M.[Marius], Shen, D.G.[Ding-Gang], Yin, J.P.[Jian-Ping], Gao, W.[Wen],
Multiple Kernel kk-Means with Incomplete Kernels,
PAMI(42), No. 5, May 2020, pp. 1191-1204.
IEEE DOI 2004
Kernel, Clustering algorithms, Optimization, Pattern analysis, Information technology, Prediction algorithms, incomplete kernel learning BibRef

Liu, X.W.[Xin-Wang], Wang, L.[Lei], Zhu, X.Z.[Xin-Zhong], Li, M.M.[Miao-Miao], Zhu, E.[En], Liu, T.L.[Tong-Liang], Liu, L.[Li], Dou, Y.[Yong], Yin, J.P.[Jian-Ping],
Absent Multiple Kernel Learning Algorithms,
PAMI(42), No. 6, June 2020, pp. 1303-1316.
IEEE DOI 2005
Kernel, Optimization, Signal processing algorithms, Clustering algorithms, Classification algorithms, max-margin classification BibRef

Tavassolipour, M.[Mostafa], Motahari, S.A.[Seyed Abolfazl], Shalmani, M.T.M.[Mohammad Taghi Manzuri],
Learning of Gaussian Processes in Distributed and Communication Limited Systems,
PAMI(42), No. 8, August 2020, pp. 1928-1941.
IEEE DOI 2007
Kernel, Training, Machine learning, Gaussian processes, Task analysis, Optimization, Distortion, Distributed learning, vector quantization BibRef

Malhotra, A.[Akshay], Schizas, I.D.[Ioannis D.],
On unsupervised simultaneous kernel learning and data clustering,
PR(108), 2020, pp. 107518.
Elsevier DOI 2008
Clustering, Matrix factorization, Correlation analysis, Kernel learning BibRef

Zhang, J.M.[Jia-Ming], Ning, H.W.[Han-Wen],
Online kernel classification with adjustable bandwidth using control-based learning approach,
PR(108), 2020, pp. 107566.
Elsevier DOI 2008
Online classification, Kernel learning, Adaptive learning, Adjustable bandwidth, Control-based approach BibRef

Li, H.L.[Hao-Liang], Li, W.[Wen], Wang, S.Q.[Shi-Qi],
Discovering and incorporating latent target-domains for domain adaptation,
PR(108), 2020, pp. 107536.
Elsevier DOI 2008
Unsupervised domain adaptation, Latent domain, Multiple kernel learning BibRef

Manna, S.[Supratim], Khonglah, J.R.[Jessy Rimaya], Mukherjee, A.[Anirban], Saha, G.[Goutam],
Robust kernelized graph-based learning,
PR(110), 2021, pp. 107628.
Elsevier DOI 2011
Robust, Clustering, Semi-supervised classification, Multiple kernels, Multiple views BibRef

Jian, M.[Meng], Jung, C.[Cheolkon],
Semi-supervised kernel matrix learning using adaptive constraint-based seed propagation,
PR(112), 2021, pp. 107750.
Elsevier DOI 2102
Adaptive constraints, Constraint propagation, Kernel learning, Seed propagation, Semi-supervised kernel matrix learning BibRef

Jia, L.L.[Lin-Lin], Gaüzère, B.[Benoit], Honeine, P.[Paul],
graphkit-learn: A Python library for graph kernels based on linear patterns,
PRL(143), 2021, pp. 113-121.
Elsevier DOI 2102
Code, Graph Kernel. Graph kernels, Linear patterns, Python implementation BibRef

Chamakura, L.[Lily], Saha, G.[Goutam],
Localized multiple kernel learning using graph modularity,
PRL(155), 2022, pp. 27-33.
Elsevier DOI 2203
Multiple kernel combining, Multi-view data, SVM, Heuristic approach, Graph modularity BibRef

Huusari, R.[Riikka], Capponi, C.[Cécile], Villoutreix, P.[Paul], Kadri, H.[Hachem],
Cross-View kernel transfer,
PR(129), 2022, pp. 108759.
Elsevier DOI 2206
Multi-view learning, Cross-view transfer, Kernel completion, Kernel learning BibRef

Alavi, F.[Fatemeh], Hashemi, S.[Sattar],
A bi-level formulation for multiple kernel learning via self-paced training,
PR(129), 2022, pp. 108770.
Elsevier DOI 2206
Multiple kernel learning, Self-paced learning, Bi-level optimization, Local kernel alignment, Global kernel alignment BibRef

Gu, B.[Bin], Dang, Z.Y.[Zhi-Yuan], Huo, Z.Y.[Zhou-Yuan], Deng, C.[Cheng], Huang, H.[Heng],
Scaling Up Generalized Kernel Methods,
PAMI(44), No. 7, July 2022, pp. 3767-3778.
IEEE DOI 2206
Kernel, Training, Stochastic processes, Convergence, Computational modeling, Scalability, Optimization, Kernel method, random feature BibRef

Chen, Y.Y.[Ying-Ying], Yang, X.W.[Xiao-Wei],
Online Adaptive Kernel Learning with Random Features for Large-scale Nonlinear Classification,
PR(131), 2022, pp. 108862.
Elsevier DOI 2208
Large-scale, Nonlinear classification, Online learning, Random feature map BibRef

Fang, K.[Kun], Liu, F.H.[Fang-Hui], Huang, X.L.[Xiao-Lin], Yang, J.[Jie],
End-to-end kernel learning via generative random Fourier features,
PR(134), 2023, pp. 109057.
Elsevier DOI 2212
Generative random Fourier features, Kernel learning, End-to-end, One-stage, Generative network, Adversarial robustness BibRef

Salim, A.[Asif], Shiju, S.S., Sumitra, S.,
Neighborhood Preserving Kernels for Attributed Graphs,
PAMI(45), No. 1, January 2023, pp. 828-840.
IEEE DOI 2212
BibRef
Earlier: A2, A1, A3:
Formulation of Two Stage Multiple Kernel Learning Using Regression Framework,
PReMI17(61-68).
Springer DOI 1711
Kernel, Support vector machines, Runtime, Feature extraction, Extraterrestrial measurements, Directed acyclic graph, graph classification. Multiple kernel learning (MKL). BibRef

Wei, P.F.[Peng-Fei], Vo, T.V.[Thanh Vinh], Qu, X.H.[Xing-Hua], Ong, Y.S.[Yew Soon], Ma, Z.J.[Ze-Jun],
Transfer Kernel Learning for Multi-Source Transfer Gaussian Process Regression,
PAMI(45), No. 3, March 2023, pp. 3862-3876.
IEEE DOI 2302
Kernel, Task analysis, Matrix decomposition, Optimization, Gaussian processes, Covariance matrices, Adaptation models, transfer kernel BibRef

Wei, P.F.[Peng-Fei], Ke, Y.P.[Yi-Ping], Ong, Y.S.[Yew-Soon], Ma, Z.J.[Ze-Jun],
Adaptive Transfer Kernel Learning for Transfer Gaussian Process Regression,
PAMI(45), No. 6, June 2023, pp. 7142-7156.
IEEE DOI 2305
Kernel, Task analysis, Matrix decomposition, Data models, Adaptation models, Standards, Semantics, Transfer regression, transfer kernel BibRef

Ortega, T.[Tomas], Jafarkhani, H.[Hamid],
Gossiped and Quantized Online Multi-Kernel Learning,
SPLetters(30), 2023, pp. 468-472.
IEEE DOI 2305
Kernel, Signal processing algorithms, Radio frequency, Quantization (signal), Mathematical models, Task analysis, sensor networks BibRef

Li, X.F.[Xing-Feng], Sun, Y.H.[Ying-Hui], Sun, Q.S.[Quan-Sen], Ren, Z.W.[Zhen-Wen],
Consensus Cluster Center Guided Latent Multi-Kernel Clustering,
CirSysVideo(33), No. 6, June 2023, pp. 2864-2876.
IEEE DOI 2306
Kernel, Sun, Eigenvalues and eigenfunctions, Task analysis, Computational complexity, Loss measurement, Convergence, consensus cluster center BibRef

Liu, L.[Li], Sun, H.C.[Hao-Cheng], Li, F.Z.[Fan-Zhang],
A Lie group kernel learning method for medical image classification,
PR(142), 2023, pp. 109735.
Elsevier DOI 2307
Medical image classification, Feature representation, Lie group manifold, Lie group machine learning, Kernel learning BibRef

Liu, J.Y.[Ji-Yuan], Liu, X.W.[Xin-Wang], Yang, Y.X.[Yue-Xiang], Liao, Q.[Qing], Xia, Y.Q.[Yuan-Qing],
Contrastive Multi-View Kernel Learning,
PAMI(45), No. 8, August 2023, pp. 9552-9566.
IEEE DOI 2307
Kernel, Hilbert space, Support vector machines, Semantics, Partitioning algorithms, Optimization, Fuses, Contrastive learning, multiple kernel clustering BibRef

Ruiz-Moreno, E.[Emilio], Beferull-Lozano, B.[Baltasar],
An Online Multiple Kernel Parallelizable Learning Scheme,
SPLetters(31), 2024, pp. 121-125.
IEEE DOI 2401
BibRef

Hicdurmaz, B.[Betul], Calik, N.[Nurullah], Ustebay, S.[Serpil],
Gauss-like Logarithmic Kernel Function to improve the performance of kernel machines on the small datasets,
PRL(179), 2024, pp. 178-184.
Elsevier DOI 2403
Kernel learning, Support vector machine, Gaussian-like kernel, Small sample size BibRef

Liu, Z.[Zheng], Huang, S.[Shiluo], Jin, W.[Wei], Mu, Y.[Ying],
Local kernels based graph learning for multiple kernel clustering,
PR(150), 2024, pp. 110300.
Elsevier DOI 2403
Kernel, Graph, Multiple kernel learning, Clustering BibRef

Liang, W.X.[Wei-Xuan], Tang, C.[Chang], Liu, X.W.[Xin-Wang], Liu, Y.[Yong], Liu, J.Y.[Ji-Yuan], Zhu, E.[En], He, K.L.[Kun-Lun],
On the Consistency and Large-Scale Extension of Multiple Kernel Clustering,
PAMI(46), No. 10, October 2024, pp. 6935-6947.
IEEE DOI 2409
Kernel, Clustering algorithms, Training, Matrix decomposition, Upper bound, Standards, Eigenvalues and eigenfunctions, large-scale extension BibRef

Liu, Y.[Yantao], Rossi, L.[Luca], Torsello, A.[Andrea],
A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures,
SSSPR22(122-131).
Springer DOI 2301
BibRef

Winter, D.[Daniel], Bian, A.[Ang], Jiang, X.Y.[Xiao-Yi],
Layer-Wise Relevance Propagation Based Sample Condensation for Kernel Machines,
CAIP21(I:487-496).
Springer DOI 2112
BibRef

Sreekar, P.A.[P. Aditya], Tiwari, U.[Ujjwal], Namboodiri, A.[Anoop],
Reducing the Variance of Variational Estimates of Mutual Information by Limiting the Critic's Hypothesis Space to RKHS,
ICPR21(10666-10674)
IEEE DOI 2105
Reproducing Hilbert Kernel Space. Neural networks, Probability distribution, Complexity theory, Random variables, Reliability BibRef

Cheng, M.[Miao], You, X.G.[Xin-Ge],
Adaptive Matching of Kernel Means,
ICPR21(2498-2505)
IEEE DOI 2105
Data analysis, Data handling, Estimation, Knowledge discovery, Complexity theory, Kernel, Intelligent systems, calculation efficiency BibRef

Zhang, Z.M.[Zi-Ming],
An Efficient Empirical Solver for Localized Multiple Kernel Learning via DNNs,
ICPR21(647-654)
IEEE DOI 2105
Training, Multilayer perceptrons, Benchmark testing, Pattern recognition, Kernel, Computational complexity BibRef

Li, J.F.[Jun-Fan], Liao, S.Z.[Shi-Zhong],
An Online Kernel Selection Wrapper via Multi-Armed Bandit Model,
ICPR18(1307-1312)
IEEE DOI 1812
Kernel, Computational modeling, Probability distribution, Learning systems, Loss measurement, Prediction algorithms, Training BibRef

Vo, P.D.[Phong D.], Sahbi, H.[Hichem],
Contextual kernel map learning for scene transduction,
ICIP15(3797-3801)
IEEE DOI 1512
BibRef
Earlier:
Modeling label dependencies in kernel learning for image annotation,
ICIP14(5886-5890)
IEEE DOI 1502
BibRef
Earlier:
Transductive inference and kernel design for object class segmentation,
ICIP12(2173-2176).
IEEE DOI 1302
context; kernel map learning; scene understanding; transductive inference. Kernel BibRef

Peluffo-Ordóñez, D.H.[Diego Hernán], Castro-Ospina, A.E.[Andrés Eduardo], Alvarado-Pérez, J.C.[Juan Carlos], Revelo-Fuelagán, E.J.[Edgardo Javier],
Multiple Kernel Learning for Spectral Dimensionality Reduction,
CIARP15(626-634).
Springer DOI 1511
BibRef

Gu, Y.F.[Yan-Feng], Wang, Q.W.[Qing-Wang], Liu, P.G.[Pi-Gang], Zuo, D.[Deshan],
Linear discriminant multiple kernel learning for multispectral image classification,
ICIP14(5052-5056)
IEEE DOI 1502
Accuracy BibRef

Guo, D.Y.[Dong-Yan], Zhang, J.[Jian], Liu, X.W.[Xin-Wang], Cui, Y.[Ying], Zhao, C.X.[Chun-Xia],
Multiple Kernel Learning Based Multi-view Spectral Clustering,
ICPR14(3774-3779)
IEEE DOI 1412
Clustering algorithms BibRef

Tonde, C.[Chetan], Elgammal, A.M.[Ahmed M.],
Simultaneous Twin Kernel Learning Using Polynomial Transformations for Structured Prediction,
CVPR14(995-1002)
IEEE DOI 1409
kernel learning BibRef

Ni, B.B.[Bing-Bing], Li, T.[Teng], Moulin, P.[Pierre],
Beta Process Multiple Kernel Learning,
CVPR14(963-970)
IEEE DOI 1409
Beta process BibRef

Uzair, M.[Muhammad], Mahmood, A.[Arif], Mian, A.S.[Ajmal S.],
Sparse Kernel Learning for Image Set Classification,
ACCV14(II: 617-631).
Springer DOI 1504
BibRef

Hino, H.[Hideitsu], Ogawa, T.[Tetsuji],
An improved entropy-based multiple kernel learning,
ICPR12(1189-1192).
WWW Link. 1302
BibRef

Liu, X.Z.[Xiao-Zhang], Feng, G.C.[Guo-Can],
Multiple kernel discriminant analysis,
ICPR12(1691-1694).
WWW Link. 1302
BibRef

Huang, K.C.[Kuan-Chieh], Lin, Y.Y.[Yen-Yu], Cheng, J.Z.[Jie-Zhi],
Cluster-dependent feature selection by multiple kernel self-organizing map,
ICPR12(589-592).
WWW Link. 1302
BibRef

Liang, Z.Z.[Zhi-Zheng], Xia, S.X.[Shi-Xiong], Liu, J.[Jin], Zhou, Y.[Yong], Zhang, L.[Lei],
A Majorization-Minimization Approach to Lq Norm Multiple Kernel Learning,
ACPR13(366-370)
IEEE DOI 1408
data handling BibRef

Gal, V.[Viktor], Kerre, E.E.[Etienne E.], Nachtegael, M.[Mike],
Multiple kernel learning based modality classification for medical images,
MCV12(76-83).
IEEE DOI 1207
BibRef

Zhang, X.Z.[Xin-Zheng], Yuan, C.G.[Cong-Gui],
Predict water quality based on multiple kernel least squares support vector regression and genetic algorithm,
ICARCV12(1597-1600).
IEEE DOI 1304
BibRef

Inoue, N.[Naoya], Yamashita, Y.[Yukihiko],
Simultaneous Learning of Localized Multiple Kernels and Classifier with Weighted Regularization,
SSSPR12(354-362).
Springer DOI 1211
BibRef

Blaschko, M.B.[Matthew B.], Lampert, C.H.[Christoph H.],
Correlational spectral clustering,
CVPR08(1-8).
IEEE DOI 0806
BibRef
And: A2, A1:
A Multiple Kernel Learning Approach to Joint Multi-class Object Detection,
DAGM08(xx-yy).
Springer DOI 0806
Award, GCPR. BibRef

Koide, N.[Naoya], Yamashita, Y.[Yukihiko],
Asymmetric kernel method and its application to Fisher's discriminant,
ICPR06(II: 820-824).
IEEE DOI 0609
BibRef

Jhuo, I.H.[I-Hong], Lee, D.T.,
Boosted Multiple Kernel Learning for Scene Category Recognition,
ICPR10(3504-3507).
IEEE DOI 1008
BibRef

Zhang, Z.M.[Zi-Ming], Li, Z.N.[Ze-Nian], Drew, M.S.[Mark S.],
AdaMKL: A Novel Biconvex Multiple Kernel Learning Approach,
ICPR10(2126-2129).
IEEE DOI 1008
BibRef

Kembhavi, A.[Aniruddha], Siddiquie, B.[Behjat], Miezianko, R.[Roland], McCloskey, S.[Scott], Davis, L.S.[Larry S.],
Incremental Multiple Kernel Learning for Object Recognition,
ICCV09(638-645).
IEEE DOI
PDF File. 0909
To obtain task specific datasets, incrementally update descriptions. BibRef

Fu, S.[Siyao], Guo, S.Y.[Sheng-Yang], Hou, Z.G.[Zeng-Guang], Liang, Z.Z.[Zi-Ze], Tan, M.[Min],
Multiple kernel learning from sets of partially matching image features,
ICPR08(1-4).
IEEE DOI 0812
SVM with multiple kernels. BibRef

Siddiquie, B.[Behjat], Vitaladevuni, S.N.[Shiv N.], Davis, L.S.[Larry S.],
Combining multiple kernels for efficient image classification,
WACV09(1-8).
IEEE DOI 0912
Multiple features for recognition, combine classifications. BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Nearest Neighbor Classification .