14.2.3 Unsupervised Clustering, Classification, Unsupervised Learning

Wu, K.L.[Kuo-Lung], Yang, M.S.[Miin-Shen],
Mean shift-based clustering,
PR(40), No. 11, November 2007, pp. 3035-3052.
Elsevier DOI 0707
kernel functions, Mean shift, Robust clustering, Generalized Epanechnikov kernel; Bandwidth selection, Parameter estimation, Mountain method; Noise
See also Alternative c-means clustering algorithms. BibRef

Wu, K.L.[Kuo-Lung], Yang, M.S.[Miin-Shen], Hsieh, J.N.[June-Nan],
Robust cluster validity indexes,
PR(42), No. 11, November 2009, pp. 2541-2550.
Elsevier DOI 0907
Cluster validity index; Clustering algorithms; Fuzzy c-means; Partition membership; Mean; Median; Robust; Noise; Outlier BibRef

Goldberger, J.[Jacob], Gordon, S., Greenspan, H.K.[Hayit K.],
Unsupervised Image-Set Clustering Using an Information Theoretic Framework,
IP(15), No. 2, February 2006, pp. 449-458.
IEEE DOI 0602
BibRef
Earlier: A2, A3, A1:
Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations,
ICCV03(370-377).
IEEE DOI 0311
BibRef
Earlier: A1, A3, A2:
Unsupervised Image Clustering Using the Information Bottleneck Method,
DAGM02(158 ff.).
Springer DOI 0303
BibRef

Johnson, S.,
Comments on 'Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis',
GeoRS(45), No. 2, February 2007, pp. 532-533.
IEEE DOI 0703

See also Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis. BibRef

Xavier, A.E.[Adilson Elias],
The hyperbolic smoothing clustering method,
PR(43), No. 3, March 2010, pp. 731-737.
Elsevier DOI 1001
Cluster analysis; Min-sum-min problems; Nondifferentiable programming; Smoothing BibRef

Xavier, A.E.[Adilson Elias], Xavier, V.L.[Vinicius Layter],
Solving the minimum sum-of-squares clustering problem by hyperbolic smoothing and partition into boundary and gravitational regions,
PR(44), No. 1, January 2011, pp. 70-77.
Elsevier DOI 1003
Cluster analysis; Min-sum-min problems; Nondifferentiable programming; Smoothing BibRef

Vatsavai, R.R.[Ranga Raju], Bhaduri, B.L.[Budhendra L.],
A hybrid classification scheme for mining multisource geospatial data,
GeoInfo(15), No. 1, January 2011, pp. 29-47.
WWW Link. 1102
BibRef

Vatsavai, R.R.[Ranga Raju], Shekhar, S.[Shashi], Bhaduri, B.L.[Budhendra L.],
A Learning Scheme for Recognizing Sub-classes from Model Trained on Aggregate Classes,
SSPR08(967-976).
Springer DOI 0812
Aggreate labels, not all sub-classes. BibRef

Patra, S.[Swarnajyoti], Bruzzone, L.[Lorenzo],
A Fast Cluster-Assumption Based Active-Learning Technique for Classification of Remote Sensing Images,
GeoRS(49), No. 5, May 2011, pp. 1617-1626.
IEEE DOI 1105
BibRef

Patra, S.[Swarnajyoti], Bruzzone, L.[Lorenzo],
A cluster-assumption based batch mode active learning technique,
PRL(33), No. 9, 1 July 2012, pp. 1042-1048.
Elsevier DOI 1202
Active learning; Cluster assumption; Entropy; Query function; Support vector machine BibRef

Demir, B., Minello, L., Bruzzone, L.,
Definition of Effective Training Sets for Supervised Classification of Remote Sensing Images by a Novel Cost-Sensitive Active Learning Method,
GeoRS(52), No. 2, February 2014, pp. 1272-1284.
IEEE DOI 1402
digital elevation models BibRef

Patra, S.[Swarnajyoti], Bruzzone, L.[Lorenzo],
A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification,
GeoRS(52), No. 11, November 2014, pp. 6899-6910.
IEEE DOI 1407
Labeling BibRef

Shin, H.C.[Hoo-Chang], Orton, M.R.[Matthew R.], Collins, D.J.[David J.], Doran, S.J.[Simon J.], Leach, M.O.[Martin O.],
Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data,
PAMI(35), No. 8, 2013, pp. 1930-1943.
IEEE DOI 1307
Feature extraction; Liver; Edge and feature detection BibRef

Pasolli, E., Melgani, F., Alajlan, N., Conci, N.,
Optical Image Classification: A Ground-Truth Design Framework,
GeoRS(51), No. 6, 2013, pp. 3580-3597.
IEEE DOI 1307
IKONOS sensors; clustering unsupervised method; hyperspectral image; support vector machines (SVMs); BibRef

Dabboor, M., Collins, M.J., Karathanassi, V., Braun, A.,
An Unsupervised Classification Approach for Polarimetric SAR Data Based on the Chernoff Distance for Complex Wishart Distribution,
GeoRS(51), No. 7, 2013, pp. 4200-4213.
IEEE DOI 1307
Eigenvalues and eigenfunctions; Radar polarimetry; terrain classification BibRef

Baldeck, C.A.[Claire A.], Asner, G.P.[Gregory P.],
Estimating Vegetation Beta Diversity from Airborne Imaging Spectroscopy and Unsupervised Clustering,
RS(5), No. 5, 2013, pp. 2057-2071.
DOI Link 1307
BibRef

Ghassabeh, Y.A.[Youness Aliyari], Linder, T.[Tamás], Takahara, G.[Glen],
On some convergence properties of the subspace constrained mean shift,
PR(46), No. 11, November 2013, pp. 3140-3147.
Elsevier DOI 1306
Unsupervised learning; Subspace constrained mean shift; Dimensionality reduction; Principal curves; Principal surfaces; Convergence BibRef

An, L.T.H.[Le Thi Hoai], Minh, L.H.[Le Hoai], Tao, P.D.[Pham Dinh],
New and efficient DCA based algorithms for minimum sum-of-squares clustering,
PR(47), No. 1, 2014, pp. 388-401.
Elsevier DOI 1310
Clustering. Difference of Convex functions Algorithm. BibRef

Hanwell, D., Mirmehdi, M.,
QUAC: Quick unsupervised anisotropic clustering,
PR(47), No. 1, 2014, pp. 427-440.
Elsevier DOI 1310
Clustering BibRef

Ghamary Asl, M., Mobasheri, M.R., Mojaradi, B.,
Unsupervised Feature Selection Using Geometrical Measures in Prototype Space for Hyperspectral Imagery,
GeoRS(52), No. 7, July 2014, pp. 3774-3787.
IEEE DOI 1403
Absorption BibRef

Fang, M.[Meng], Zhu, X.Q.[Xing-Quan],
Active learning with uncertain labeling knowledge,
PRL(43), No. 1, 2014, pp. 98-108.
Elsevier DOI 1404
BibRef
Earlier:
I don't know the label: Active learning with blind knowledge,
ICPR12(2238-2241).
WWW Link. 1302
Award, ICPR. Active learning BibRef

Pan, S.R.[Shi-Rui], Zhu, X.Q.[Xing-Quan], Fang, M.[Meng],
Top-k correlated subgraph query for data streams,
ICPR12(2906-2909).
WWW Link. 1302
BibRef

Rad, A.M.[Amir Moeini], Abkar, A.A.[Ali Akbar], Mojaradi, B.[Barat],
Supervised Distance-Based Feature Selection for Hyperspectral Target Detection,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

You, X., Wang, R., Tao, D.,
Diverse Expected Gradient Active Learning for Relative Attributes,
IP(23), No. 7, July 2014, pp. 3203-3217.
IEEE DOI 1407
Optimization Semantic understanding for HCI. BibRef

Feng, J.S.[Jia-Shi], Yuan, X.T.[Xiao-Tong], Wang, Z.L.[Zi-Lei], Xu, H.[Huan], Yan, S.C.[Shui-Cheng],
Autogrouped Sparse Representation for Visual Analysis,
IP(23), No. 12, December 2014, pp. 5390-5399.
IEEE DOI 1412
BibRef
Earlier:
Auto-Grouped Sparse Representation for Visual Analysis,
ECCV12(I: 640-653).
Springer DOI 1210
image classification BibRef

Wang, W.Q.[Wen-Qi], Aggarwal, V.[Vaneet], Aeron, S.C.[Shu-Chin],
Unsupervised clustering under the Union of Polyhedral Cones (UOPC) model,
PRL(100), No. 1, 2017, pp. 104-109.
Elsevier DOI 1712
BibRef

Chang, J.L.[Jian-Long], Wang, L.F.[Ling-Feng], Meng, G.F.[Gao-Feng], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Deep unsupervised learning with consistent inference of latent representations,
PR(77), 2018, pp. 438-453.
Elsevier DOI 1802
Deep unsupervised learning, Consistent inference of latent representations BibRef

Chang, J.L.[Jian-Long], Meng, G.F.[Gao-Feng], Wang, L.F.[Ling-Feng], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Deep Self-Evolution Clustering,
PAMI(42), No. 4, April 2020, pp. 809-823.
IEEE DOI 2003
Task analysis, Unsupervised learning, Training, Clustering methods, Pattern analysis, Clustering, deep self-evolution clustering, deep unsupervised learning BibRef

Suo, M.L.[Ming-Liang], An, R.M.[Ruo-Ming], Zhou, D.[Ding], Li, S.L.[Shun-Li],
Grid-clustered rough set model for self-learning and fast reduction,
PRL(106), 2018, pp. 61-68.
Elsevier DOI 1804
Rough set, Rough self-learning, Fast reduction, Grid cluster BibRef

Malhotra, A., Schizas, I.D.,
MILP-Based Unsupervised Clustering,
SPLetters(25), No. 12, December 2018, pp. 1825-1829.
IEEE DOI 1812
gradient methods, hyperspectral imaging, integer programming, linear programming, matrix decomposition, pattern clustering, clustering BibRef

Merkurjev, E.[Ekaterina],
A fast graph-based data classification method with applications to 3D sensory data in the form of point clouds,
PRL(136), 2020, pp. 154-160.
Elsevier DOI 2008
Data classification 1, Graph-based setting, Optimization, Auction dynamics, Sensory data BibRef

Pedronette, D.C.G.[Daniel Carlos Guimarães], Valem, L.P.[Lucas Pascotti], da Silva Torres, R.[Ricardo],
A BFS-Tree of ranking references for unsupervised manifold learning,
PR(111), 2021, pp. 107666.
Elsevier DOI 2012
Content-based image retrieval, Unsupervised manifold learning, Tree representation, Ranking references BibRef

Valem, L.P.[Lucas Pascotti], Pedronette, D.C.G.[Daniel Carlos Guimarães], Latecki, L.J.[Longin Jan],
Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning,
IP(32), 2023, pp. 2811-2826.
IEEE DOI 2305
Task analysis, Manifold learning, Feature extraction, Manifolds, Convolutional neural networks, Technological innovation, person Re-ID BibRef

Cai, J.Y.[Jin-Yu], Wang, S.P.[Shi-Ping], Xu, C.[Chao=Yang], Guo, W.Z.[Wen-Zhong],
Unsupervised deep clustering via contractive feature representation and focal loss,
PR(123), 2022, pp. 108386.
Elsevier DOI 2112
Unsupervised learning, Clustering, Contractive feature representation, Focal loss, Auto-encoder BibRef

Borna, K.[Kambiz], Moore, A.B.[Antoni B.], Hoshyar, A.N.[Azadeh Noori], Sirguey, P.[Pascal],
Using Vector Agents to Implement an Unsupervised Image Classification Algorithm,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Ye, M.[Mang], Shen, J.B.[Jian-Bing], Zhang, X.[Xu], Yuen, P.C.[Pong C.], Chang, S.F.[Shih-Fu],
Augmentation Invariant and Instance Spreading Feature for Softmax Embedding,
PAMI(44), No. 2, February 2022, pp. 924-939.
IEEE DOI 2201
BibRef
Earlier: A1, A3, A4, A5, Only:
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature,
CVPR19(6203-6212).
IEEE DOI 2002
Task analysis, Visualization, Testing, Training, Unsupervised learning, Data mining, Unsupervised learning, data augmentation BibRef

Chen, S.[Song], Xue, J.H.[Jing-Hao], Chang, J.L.[Jian-Long], Zhang, J.Z.[Jian-Zhong], Yang, J.F.[Ju-Feng], Tian, Q.[Qi],
SSL++: Improving Self-Supervised Learning by Mitigating the Proxy Task-Specificity Problem,
IP(31), 2022, pp. 1134-1148.
IEEE DOI 2202
Task analysis, Representation learning, Semantics, Feature extraction, Visualization, Noise measurement, Couplings, convolutional neural networks BibRef

Wang, J.Y.[Jing-Yu], Ma, Z.Y.[Zhen-Yu], Nie, F.P.[Fei-Ping], Li, X.L.[Xue-Long],
Entropy regularization for unsupervised clustering with adaptive neighbors,
PR(125), 2022, pp. 108517.
Elsevier DOI 2203
Unsupervised clustering, Similarity matrix, Entropy regularization, Trivial similarity distribution, Adaptive neighbors BibRef

Chatterjee, A.[Ankita], Saha, J.[Jayasree], Mukherjee, J.[Jayanta],
Clustering with multi-layered perceptron,
PRL(155), 2022, pp. 92-99.
Elsevier DOI 2203
Multi layer perceptron, Unsupervised learning, Clustering BibRef

Lu, H.[Hu], Chen, C.[Chao], Wei, H.[Hui], Ma, Z.C.[Zhong-Chen], Jiang, K.[Ke], Wang, Y.Q.[Ying-Quan],
Improved deep convolutional embedded clustering with re-selectable sample training,
PR(127), 2022, pp. 108611.
Elsevier DOI 2205
Unsupervised clustering, Deep embedded clustering, Autoencoder, Reliable samples BibRef

Wang, J.H.[Jing-Hua], Wang, L.[Li], Jiang, J.M.[Jian-Min],
Preserving similarity order for unsupervised clustering,
PR(128), 2022, pp. 108670.
Elsevier DOI 2205
Image clustering, Order preserving, Deep representation learning, Score function learning BibRef

Fan, J.F.[Jin-Fu], Yu, Y.[Yang], Wang, Z.J.[Zhong-Jie], Gu, J.Y.[Jin-Yi],
Partial Label Learning Based on Disambiguation Correction Net With Graph Representation,
CirSysVideo(32), No. 8, August 2022, pp. 4953-4967.
IEEE DOI 2208
Phase locked loops, Entropy, Training, Data models, Integrated circuit modeling, Deep learning, Transforms, label probability threshold BibRef

Fan, J.F.[Jin-Fu], Yu, Y.[Yang], Huang, L.Q.[Lin-Qing], Wang, Z.J.[Zhong-Jie],
GraphDPI: Partial label disambiguation by graph representation learning via mutual information maximization,
PR(134), 2023, pp. 109133.
Elsevier DOI 2212
Partial label learning, GraphDPI, Mutual Information, Triplet loss BibRef

Fan, J.F.[Jin-Fu], Wang, Z.J.[Zhong-Jie],
Partial Label Learning via GANs With Multiclass SVMs and Information Maximization,
CirSysVideo(32), No. 12, December 2022, pp. 8409-8421.
IEEE DOI 2212
Phase locked loops, Generators, Semantics, Supervised learning, Predictive models, Neural networks, Training, partial contrastive loss BibRef

Feigin, Y.[Yuri], Spitzer, H.[Hedva], Giryes, R.[Raja],
Cluster with GANs,
CVIU(225), 2022, pp. 103571.
Elsevier DOI 2212
Combine them. Unsupervised learning. GAN, clustering BibRef

Kim, J.[Jeonghoon], Im, S.H.[Sung-Hoon], Cho, S.[Sunghyun],
ProFeat: Unsupervised image clustering via progressive feature refinement,
PRL(164), 2022, pp. 166-172.
Elsevier DOI 2212
Clustering, Unsupervised learning, Representation learning BibRef

Pastore, V.P.[Vito Paolo], Ciranni, M.[Massimiliano], Bianco, S.[Simone], Fung, J.C.[Jennifer Carol], Murino, V.[Vittorio], Odone, F.[Francesca],
Efficient unsupervised learning of biological images with compressed deep features,
IVC(137), 2023, pp. 104764.
Elsevier DOI 2309
Unsupervised learning, Transfer learning, Biological images analysis, Pre-trained features, Model ensembling BibRef

Guo, Y.H.[Yun-Hui], Zhang, Y.[Youren], Chen, Y.[Yubei], Yu, S.X.[Stella X.],
Unsupervised Feature Learning with Emergent Data-Driven Prototypicality,
CVPR24(23199-23208)
IEEE DOI 2410
Representation learning, Training, Visualization, Computer hacking, Computational modeling, Extraterrestrial measurements BibRef

Li, A.C.[Alexander C.], Brown, E.[Ellis], Efros, A.A.[Alexei A.], Pathak, D.[Deepak],
Internet Curiosity: Directed Unsupervised Learning on Uncurated Internet Data,
SelfLearn22(100-104).
Springer DOI 2304
BibRef

Pang, B.[Bo], Zhang, Y.F.[Yi-Fan], Li, Y.[Yaoyi], Cai, J.[Jia], Lu, C.[Cewu],
Unsupervised Visual Representation Learning by Synchronous Momentum Grouping,
ECCV22(XXX:265-282).
Springer DOI 2211
BibRef

Zhang, W.W.[Wen-Wei], Pang, J.M.[Jiang-Miao], Chen, K.[Kai], Loy, C.C.[Chen Change],
Dense Siamese Network for Dense Unsupervised Learning,
ECCV22(XXX:464-480).
Springer DOI 2211
BibRef

Fostiropoulos, I.[Iordanis], Boehm, B.[Barry],
Implicit Feature Decoupling with Depthwise Quantization,
CVPR22(396-405)
IEEE DOI 2210
Quantization (signal), Tensors, Statistical analysis, Redundancy, Neural networks, Estimation, Statistical methods, Self- semi- meta- unsupervised learning BibRef

Li, Z.[Zefan], Liu, C.X.[Chen-Xi], Yuille, A.L.[Alan L.], Ni, B.B.[Bing-Bing], Zhang, W.J.[Wen-Jun], Gao, W.[Wen],
Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement,
CVPR21(9762-9771)
IEEE DOI 2111
Training, Supervised learning, Feature extraction, Data mining, Task analysis BibRef

Dang, Z.Y.[Zhi-Yuan], Deng, C.[Cheng], Yang, X.[Xu], Wei, K.[Kun], Huang, H.[Heng],
Nearest Neighbor Matching for Deep Clustering,
CVPR21(13688-13697)
IEEE DOI 2111
Codes, Semantics, Artificial neural networks, Benchmark testing, Unsupervised learning BibRef

Xie, Z.D.[Zhen-Da], Lin, Y.T.[Yu-Tong], Zhang, Z.[Zheng], Cao, Y.[Yue], Lin, S.[Stephen], Hu, H.[Han],
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning,
CVPR21(16679-16688)
IEEE DOI 2111
Learning systems, Visualization, Head, Codes, Semantics, Object detection BibRef

Park, S.[Sungwon], Han, S.[Sungwon], Kim, S.[Sundong], Kim, D.[Danu], Park, S.[Sungkyu], Hong, S.[Seunghoon], Cha, M.[Meeyoung],
Improving Unsupervised Image Clustering With Robust Learning,
CVPR21(12273-12282)
IEEE DOI 2111
Training, Clustering methods, Computational modeling, Predictive models, Robustness, Calibration BibRef

Henzler, P.[Philipp], Reizenstein, J.[Jeremy], Labatut, P.[Patrick], Shapovalov, R.[Roman], Ritschel, T.[Tobias], Vedaldi, A.[Andrea], Novotny, D.[David],
Unsupervised Learning of 3D Object Categories from Videos in the Wild,
CVPR21(4698-4707)
IEEE DOI 2111
Training, Surface reconstruction, Benchmark testing, Surface texture, Image reconstruction BibRef

Zhang, Y.F.[Yi-Fei], Liu, C.[Chang], Zhou, Y.[Yu], Wang, W.[Wei], Wang, W.P.[Wei-Ping], Ye, Q.X.[Qi-Xiang],
Progressive Cluster Purification for Unsupervised Feature Learning,
ICPR21(8476-8483)
IEEE DOI 2105
Training, Purification, Filtering, Clustering methods, Focusing, Benchmark testing, Reliability BibRef

Aizawa, H.[Hiroaki], Kataoka, H.[Hirokatsu], Satoh, Y.[Yutaka], Kato, K.[Kunihito],
Disentangle, Assemble, and Synthesize: Unsupervised Learning to Disentangle Appearance and Location,
ICPR21(2065-2072)
IEEE DOI 2105
Image resolution, Image synthesis, Computational modeling, Generative adversarial networks, BibRef

Wang, R.[Ru], Li, L.[Lin], Wang, P.P.[Pei-Pei], Tao, X.H.[Xiao-Hui], Liu, P.[Peiyu],
Feature-aware unsupervised learning with joint variational attention and automatic clustering,
ICPR21(923-930)
IEEE DOI 2105
Clustering methods, Data mining, Task analysis, Optimization, Unsupervised learning BibRef

Han, S.[Sungwon], Park, S.[Sungwon], Park, S.K.[Sung-Kyu], Kim, S.[Sundong], Cha, M.[Meeyoung],
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification,
ECCV20(XXIV:768-784).
Springer DOI 2012
BibRef

Abukmeil, M., Ferrari, S., Genovese, A., Piuri, V., Scotti, F.,
Unsupervised Learning From Limited Available Data by ß-NMF and Dual Autoencoder,
ICIP20(81-85)
IEEE DOI 2011
Unsupervised Learning, Limited data learning, Non-negative Matrix Factorization, Autoencoder BibRef

van Gansbeke, W.[Wouter], Vandenhende, S.[Simon], Georgoulis, S.[Stamatios], Proesmans, M.[Marc], Van Gool, L.J.[Luc J.],
Scan: Learning to Classify Images Without Labels,
ECCV20(X:268-285).
Springer DOI 2011
Code, Classification.
WWW Link. BibRef

Newell, A.[Alejandro], Deng, J.[Jia],
How Useful Is Self-Supervised Pretraining for Visual Tasks?,
CVPR20(7343-7352)
IEEE DOI 2008
Code, Pretraining.
WWW Link. Task analysis, Training, Image color analysis, Data models, Complexity theory, Visualization, Benchmark testing BibRef

Ye, M., Shen, J.,
Probabilistic Structural Latent Representation for Unsupervised Embedding,
CVPR20(5456-5465)
IEEE DOI 2008
Training, Probabilistic logic, Testing, Task analysis, Feature extraction, Robustness, Visualization BibRef

Gao, X., Hu, W., Qi, G.,
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-Wise Transformations,
CVPR20(7161-7170)
IEEE DOI 2008
Decoding, Image reconstruction, Feature extraction, Convolution, Anisotropic magnetoresistance BibRef

Ji, X., Vedaldi, A.[Andrea], Henriques, J.,
Invariant Information Clustering for Unsupervised Image Classification and Segmentation,
ICCV19(9864-9873)
IEEE DOI 2004
image classification, image representation, image segmentation, neural nets, pattern clustering, Entropy BibRef

Sun, C.[Chen], Myers, A.[Austin], Vondrick, C.[Carl], Murphy, K.[Kevin], Schmid, C.[Cordelia],
VideoBERT: A Joint Model for Video and Language Representation Learning,
ICCV19(7463-7472)
IEEE DOI 2004
image classification, learning (artificial intelligence), social networking (online), speech recognition, BibRef

Feng, Z.[Zeyu], Xu, C.[Chang], Tao, D.C.[Da-Cheng],
Self-Supervised Representation Learning From Multi-Domain Data,
ICCV19(3244-3254)
IEEE DOI 2004
image representation, knowledge management, learning (artificial intelligence), knowledge transfer, Picture archiving and communication systems BibRef

Nina, O.[Oliver], Moody, J.[Jamison], Milligan, C.[Clarissa],
A Decoder-Free Approach for Unsupervised Clustering and Manifold Learning with Random Triplet Mining,
CLI19(3987-3994)
IEEE DOI 2004
data mining, neural net architecture, pattern clustering, unsupervised learning, cluster image samples, offline training, manifold learning BibRef

Zhuo, J.B.[Jun-Bao], Wang, S.H.[Shu-Hui], Cui, S.H.[Shu-Hao], Huang, Q.M.[Qing-Ming],
Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization,
CVPR19(750-759).
IEEE DOI 2002
Deal with unknown categories. BibRef

Nguyen, T.G.L.[The-Gia Leo], Ardizzone, L.[Lynton], Köthe, U.[Ullrich],
Training Invertible Neural Networks as Autoencoders,
GCPR19(442-455).
Springer DOI 1911
BibRef

Suzuki, T.[Tomoyuki], Itazuri, T.[Takahiro], Hara, K.[Kensho], Kataoka, H.[Hirokatsu],
Learning Spatiotemporal 3D Convolution with Video Order Self-supervision,
PersonContext18(II:590-598).
Springer DOI 1905
self-supervised learning. BibRef

Lee, H., Kim, T., Song, E., Lee, S.,
Collabonet: Collaboration of Generative Models by Unsupervised Classification,
ICIP18(1068-1072)
IEEE DOI 1809
Task analysis, Mathematical model, Data models, Manganese, Image reconstruction, Cats, Unsupervised learning, Ensemble model BibRef

Ya, H., Sun, H., Helt, J., Lee, T.S.,
Learning to Associate Words and Images Using a Large-Scale Graph,
CRV17(16-23)
IEEE DOI 1804
convolution, feedforward neural nets, graph theory, image resolution, railways, unsupervised learning, unsupervised learning BibRef

Meng, F., Liu, H., Liang, Y., Wei, L., Pei, J.,
A bidirectional adaptive bandwidth mean shift strategy for clustering,
ICIP17(2448-2452)
IEEE DOI 1803
Bandwidth, Clustering algorithms, Convergence, Frequency modulation, Information technology, Kernel, Mean Shift BibRef

Hoang, T., Do, T.T., Le Tan, D.K., Cheung, N.M.,
Enhancing feature discrimination for unsupervised hashing,
ICIP17(3710-3714)
IEEE DOI 1803
Covariance matrices, Gaussian mixture model, Iterative methods, Measurement, Principal component analysis, Quantization (signal), Hashing BibRef

En, S., Crémilleux, B., Jurie, F.,
Unsupervised deep hashing with stacked convolutional autoencoders,
ICIP17(3420-3424)
IEEE DOI 1803
Binary codes, Convolutional codes, Decoding, Image reconstruction, Semantics, Task analysis, Unsupervised learning, unsupervised learning BibRef

Prabhushankar, M., Temel, D., Al Regib, G.,
Generating adaptive and robust filter sets using an unsupervised learning framework,
ICIP17(3041-3045)
IEEE DOI 1803
Correlation, Covariance matrices, Image quality, Neural networks, Task analysis, Testing, Unsupervised learning, ZCA Whitening BibRef

Jenni, S.[Simon], Meishvili, G.[Givi], Favaro, P.[Paolo],
Video Representation Learning by Recognizing Temporal Transformations,
ECCV20(XXVIII:425-442).
Springer DOI 2011
BibRef

Noroozi, M.[Mehdi], Pirsiavash, H.[Hamed], Favaro, P.[Paolo],
Representation Learning by Learning to Count,
ICCV17(5899-5907)
IEEE DOI 1802
image representation, learning (artificial intelligence), neural nets, artificial supervision signal, Visualization BibRef

Hassanzadeh, A.[Aidin], Kaarna, A.[Arto], Kauranne, T.[Tuomo],
Unsupervised Multi-manifold Classification of Hyperspectral Remote Sensing Images with Contractive Autoencoder,
SCIA17(II: 169-180).
Springer DOI 1706
BibRef

Mukuta, Y.[Yusuke], Harada, T.[Tatsuya],
Kernel Approximation via Empirical Orthogonal Decomposition for Unsupervised Feature Learning,
CVPR16(5222-5230)
IEEE DOI 1612
BibRef

Pathak, D.[Deepak], Krähenbühl, P.[Philipp], Donahue, J.[Jeff], Darrell, T.J.[Trevor J.], Efros, A.A.[Alexei A.],
Context Encoders: Feature Learning by Inpainting,
CVPR16(2536-2544)
IEEE DOI 1612
BibRef

Yang, J., Parikh, D., Batra, D.,
Joint Unsupervised Learning of Deep Representations and Image Clusters,
CVPR16(5147-5156)
IEEE DOI 1612
BibRef

Li, Y., Huang, C., Tang, X., Loy, C.C.,
Learning to Disambiguate by Asking Discriminative Questions,
ICCV17(3439-3448)
IEEE DOI 1802
BibRef
Earlier: A2, A4, A3, Only:
Unsupervised Learning of Discriminative Attributes and Visual Representations,
CVPR16(5175-5184)
IEEE DOI 1612
image retrieval, learning (artificial intelligence), question answering (information retrieval), Visualization BibRef

Wang, J.[Jun_Hong], Miao, Y.[Yun_Qian], Khamis, A.[Alaa], Karray, F.[Fakhri], Liang, J.[Jiye],
Adaptation Approaches in Unsupervised Learning: A Survey of the State-of-the-Art and Future Directions,
ICIAR16(3-11).
Springer DOI 1608
BibRef

Afonso, M.[Mariana], Teixeira, L.F.[Luis F.],
Experimental Evaluation of the Bag-of-Features Model for Unsupervised Learning of Images,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Tariq, A.[Amara], Foroosh, H.[Hassan],
T-clustering: Image clustering by tensor decomposition,
ICIP15(4803-4807)
IEEE DOI 1512
Image Clustering. Takes into account the spatial configuration of images. BibRef

Grozavu, N.[Nistor], Rogovschi, N.[Nicoleta], Cabanes, G.[Guenael], Troya-Galvis, A.[Andres], Gancarski, P.[Pierre],
VHR satellite image segmentation based on topological unsupervised learning,
MVA15(543-546)
IEEE DOI 1507
Clustering algorithms BibRef

Jhuo, I.H.[I-Hong], Gao, S.H.[Sheng-Hua], Zhuang, L.S.[Lian-Sheng], Lee, D.T., Ma, Y.[Yi],
Unsupervised Feature Learning for RGB-D Image Classification,
ACCV14(I: 276-289).
Springer DOI 1504
BibRef

Momtaz, R.[Rana], Mohssen, N.[Nesma], Gowayyed, M.A.[Mohammad A.],
DWOF: A Robust Density-Based Outlier Detection Approach,
IbPRIA13(517-525).
Springer DOI 1307
BibRef

Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Ensemble Projection for Semi-supervised Image Classification,
ICCV13(2072-2079)
IEEE DOI 1403
Ensemble Learning BibRef

Dai, D.X.[Deng-Xin], Prasad, M.[Mukta], Leistner, C.[Christian], Van Gool, L.J.[Luc J.],
Ensemble Partitioning for Unsupervised Image Categorization,
ECCV12(III: 483-496).
Springer DOI 1210
BibRef

Pan, G.D.[Guo-Dong], Shang, L.F.[Li-Feng], Schnieders, D.[Dirk], Wong, K.Y.K.[Kwan-Yee K.],
Mode Seeking with an Adaptive Distance Measure,
ARTEMIS12(III: 213-222).
Springer DOI 1210
BibRef

He, P.[Ping], Xu, X.H.[Xiao-Hua], Chen, L.[Ling],
Constrained Clustering with Local Constraint Propagation,
ARTEMIS12(III: 223-232).
Springer DOI 1210
BibRef

Hu, D.[Diane], Bo, L.F.[Lie-Feng], Ren, X.F.[Xiao-Feng],
Toward Robust Material Recognition for Everyday Objects,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Oliveira, L.O.V.B.[Luiz Otávio Vilas Boas], Drummond, I.N.[Isabela Neves],
Real-Valued Negative Selection (RNS) for Classification Task,
ICPR-Contests10(66-74).
Springer DOI 1008
BibRef

Li, Z.W.[Zhong-Wei], Pan, Z.K.[Zhen-Kuan], Ni, M.J.[Ming-Jiu],
An unsupervised model for image classification,
IASP10(38-40).
IEEE DOI 1004
For tracking deformable objects. BibRef

Shapira, L.[Lior], Avidan, S.[Shai], Shamir, A.[Ariel],
Mode-detection via median-shift,
ICCV09(1909-1916).
IEEE DOI 0909
BibRef

Chen, Y.L.[Yen-Lun], Zheng, Y.F.[Yuan F.],
Margin and domain integrated classification,
ICIP09(2061-2064).
IEEE DOI 0911
BibRef

Cruz, B.[Benjamín], Barrón, R.[Ricardo], Sossa, H.[Humberto],
A New Unsupervised Learning for Clustering Using Geometric Associative Memories,
CIARP09(239-246).
Springer DOI 0911
BibRef

Gilani, S.Z.[Syed Zulqarnain], Rao, N.I.[Naveed Iqbal],
Fast Block Clustering Based Optimized Adaptive Mediod Shift,
CAIP09(435-443).
Springer DOI 0909
BibRef

Singh, A.[Abhishek], Jaikumar, P.[Padmini], Mitra, S.K.[Suman K.],
A Bayesian Learning Based Approach for Clustering of Satellite Images,
ICCVGIP08(187-192).
IEEE DOI 0812
BibRef

Chang, Y.C.[Yu-Chou], Lee, D.J.[Dah-Jye], Archibald, J.[James], Hong, Y.[Yi],
Unsupervised clustering using hyperclique pattern constraints,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Bai, X.X.[Xin-Xin], Chen, G.[Gang], Lin, Z.L.[Zhong-Lin], Yin, W.J.[Wen-Jun], Dong, J.[Jin],
Improving image clustering: An unsupervised feature weight learning framework,
ICIP08(977-980).
IEEE DOI 0810
BibRef

Gibert, K.[Karina], Rodríguez Silva, G.[Gustavo],
Identification of More Characteristic Dynamic Patterns in a WWTP by CIBRxE,
CIARP08(372-380).
Springer DOI 0809
BibRef

Inoue, K.[Kohei], Urahama, K.[Kiichi],
Hierarchically Distributed Dynamic Mean Shift,
ICIP07(I: 269-272).
IEEE DOI 0709
Iterative mode seeking algorithm. A less memory intensive implementation. BibRef

Lange, T.[Tilman], Law, M.H.C.[Martin H.C.], Jain, A.K.[Anil K.], Buhmann, J.M.[Joachim M.],
Learning with Constrained and Unlabelled Data,
CVPR05(I: 731-738).
IEEE DOI 0507
BibRef

Furao, S.[Shen], Hasegawa, O.[Osamu],
An On-Line Learning Mechanism for Unsupervised Classification and Topology Representation,
CVPR05(I: 651-656).
IEEE DOI 0507
BibRef

Robles-Kelly, A.[Antonio], Hancock, E.R.,
Pairwise Clustering with Matrix Factorisation and the EM Algorithm,
ECCV02(II: 63 ff.).
Springer DOI 0205
for grouping via pairwise clustering. BibRef

Zhu, Y., Comaniciu, D., Schwartz, S., Ramesh, V.,
Multimodal Data Representations with Parameterized Local Structures,
ECCV02(I: 173 ff.).
Springer DOI 0205
BibRef

Boujemaa, N.[Nozha],
On Competitive Unsupervised Clustering,
ICPR00(Vol I: 631-634).
IEEE DOI 0009
For segmentation. BibRef

Nowak, R.D., Figueiredo, M.A.T.,
Unsupervised Segmentation of Poisson Data,
ICPR00(Vol III: 155-158).
IEEE DOI 0009
BibRef

Stauffer, C.[Chris],
Minimally-supervised classification using multiple observation sets,
ICCV03(297-304).
IEEE DOI 0311
BibRef

Stauffer, C.[Chris],
Minimally Supervised Classification,
DARPA98(145-150). BibRef 9800

Fränti, P., Kivijärvi, J.,
Random Swapping Technique for Improving Clustering in Unsupervised Classification,
SCIA99(Pattern Recognition I). BibRef 9900

Descombes, X., Kruggel, F., Palubinskas, G.[Gintautas],
An Unsupervised Clustering Method Using the Entropy Minimization,
ICPR98(Vol II: 1816-1818).
IEEE DOI 9808
BibRef

Renyi, A.,
On Measures of Entropy and Information,
ConferenceBerkeley Symposium Mathematics, Statistics, and Probability, 1960, pp. 547-561. BibRef 6000

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

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