14.2.3 Unsupervised Clustering, Classification

Chapter Contents (Back)
Unsupervised.

Shaffer, E.[Edward], Dubes, R.C.[Richard C.], Jain, A.K.[Anil K.],
Single-link characteristics of a mode-seeking clustering algorithm,
PR(11), No. 1, 1979, pp. 65-70.
Elsevier DOI 0309
BibRef

Kittler, J.V.[Josef V.],
Comments on 'single-link characteristics of a mode-seeking clustering algorithm',
PR(11), No. 1, 1979, pp. 71-73.
Elsevier DOI 0309
BibRef

Pathak, A.P.[A. Pal], Pal, S.K.,
Generalized guard-zone algorithm (GGA) for learning: automatic selection of threshold,
PR(23), No. 3-4, 1990, pp. 325-335.
Elsevier DOI 0401
self-supervised parameter learning. BibRef

LeHegarat-Mascle, S., Bloch, I., Vidal-Madjar, D.,
Application of Dempster-Shafer Evidence Theory to Unsupervised Classification in Multisource Remote Sensing,
GeoRS(35), No. 4, July 1997, pp. 1018-1031.
IEEE Top Reference. 9708
See also Mathematical Theory of Evidence, A. BibRef

Lee, J.S., Grunes, M.R., Ainsworth, T.L., Du, L.J., Schuler, D.L., Cloude, S.R.,
Unsupervised Classification Using Polarimetric Decomposition and the Complex Wishart Classifier,
GeoRS(37), No. 5, September 1999, pp. 2249. BibRef 9909

Du, L.J., Grunes, M.R., Lee, J.S.,
Unsupervised segmentation of dual-polarization SAR images based on amplitude and texture characteristics,
JRS(23), No. 20, October 2002, pp. 4383-4402.
WWW Link. 0211
BibRef

Brumbley, C.[Clark], Chang, C.I.[Chein-I],
An unsupervised vector quantization-based target subspace projection approach to mixed pixel detection and classification in unknown background for remotely sensed imagery,
PR(32), No. 7, July 1999, pp. 1161-1174.
Elsevier DOI BibRef 9907

Ren, H., Chang, C.I.[Chein-I],
A Generalized Orthogonal Subspace Projection Approach to Unsupervised Multispectral Image Classification,
GeoRS(38), No. 6, November 2000, pp. 2515-2528.
IEEE Top Reference. 0011
See also Anomaly detection and classification for hyperspectral imagery. BibRef

Chang, C.I.,
Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis,
GeoRS(43), No. 3, March 2005, pp. 502-518.
IEEE Abstract. 0501
See also Comments on Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis. BibRef

Roberts, S.J.[Stephen J.], Holmes, C.[Chris], Denison, D.[Dave],
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo,
PAMI(23), No. 8, August 2001, pp. 909-914.
IEEE DOI 0109
In unsupervised classifications, how to find the best partition. Hence, use entropy measures. BibRef

Gokcay, E.[Erhan], Principe, J.C.[Jose C.],
Information Theoretic Clustering,
PAMI(24), No. 2, February 2002, pp. 158-171.
IEEE DOI 0202
Applied to MRI data. Derived from Renyi's measure. See also On Measures of Entropy and Information. BibRef

Yeung, D.S., Wang, X.Z.,
Improving Performance of Similarity-Based Clustering by Feature Weight Learning,
PAMI(24), No. 4, April 2002, pp. 556-561.
IEEE DOI 0204
learning feature weights for classification. See also Improving Fuzzy C-Means Clustering Based on Feature-Weight Learning. BibRef

Duda, T., Canty, M.,
Unsupervised Classification of Satellite Imagery: Choosing a Good Algorithm,
JRS(23), No. 11, June 2002, pp. 2193-2212.
WWW Link. 0206
BibRef

Canty, M.J.,
Boosting a fast neural network for supervised land cover classification,
CompGeo(35), No. 6, 2009, pp. 1280-1295
Elsevier DOI 1102
BibRef

Garai, G.[Gautam], Chaudhuri, B.B.,
A novel genetic algorithm for automatic clustering,
PRL(25), No. 2, January 2004, pp. 173-187.
Elsevier DOI 0401
BibRef

Frigui, H.[Hichem], Nasraoui, O.[Olfa],
Unsupervised learning of prototypes and attribute weights,
PR(37), No. 3, March 2004, pp. 567-581.
Elsevier DOI 0401
BibRef

Tasoulis, D.K., Vrahatis, M.N.,
Unsupervised clustering on dynamic databases,
PRL(26), No. 13, 1 October 2005, pp. 2116-2127.
Elsevier DOI 0509
BibRef

Yang, M.S.[Miin-Shen], Wu, K.L.[Kuo-Lung],
Unsupervised possibilistic clustering,
PR(39), No. 1, January 2006, pp. 5-21.
Elsevier DOI 0512
BibRef

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

Wu, M.R.[Ming-Rui], Ye, J.P.[Jie-Ping],
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers,
PAMI(31), No. 11, November 2009, pp. 2088-2092.
IEEE DOI 0910
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

Xiao, Y.C.[Ying-Chao], Wang, H.G.[Huan-Gang], Xu, W.L.[Wen-Li], Zhou, J.[Junwu],
L1 norm based KPCA for novelty detection,
PR(46), No. 1, January 2013, pp. 389-396.
Elsevier DOI 1209
KPCA; L1 norm; Novelty detection One class classification problem 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

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.[Jiashi], Yuan, X.T.[Xiao-Tong], Wang, Z.[Zilei], 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.[Shuchin],
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

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

Vo, B.N.[Ba-Ngu], Dam, N.[Nhan], Phung, D.[Dinh], Tran, N.Q.[Nhat-Quang], Vo, B.T.[Ba-Tuong],
Model-based learning for point pattern data,
PR(84), 2018, pp. 136-151.
Elsevier DOI 1809
BibRef
Earlier: A1, A4, A3, A5, Only:
Model-Based Classification and Novelty Detection for Point Pattern Data,
ICPR16(2622-2627)
IEEE DOI 1705
BibRef
And: A4, A1, A3, A5, Only:
Clustering for point pattern data,
ICPR16(3174-3179)
IEEE DOI 1705
Point pattern, Point process, Random finite set, Multiple instance learning, Classification, Novelty detection, Clustering. Computational modeling, Data models, Maximum likelihood estimation, Measurement units, Niobium, Radio frequency, Training data, multiple instance data, naive Bayes model. Data models, Feature extraction, Indexes, Measurement, Clustering, affinity propagation, expectation-maximization. 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

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

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.[Shirui], Zhu, X.Q.[Xing-Quan], Fang, M.[Meng],
Top-k correlated subgraph query for data streams,
ICPR12(2906-2909).
WWW Link. 1302
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.[Lifeng], 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, Z.[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

Sudo, K.[Kyoko], Osawa, T.[Tatsuya], Tanaka, H.[Hidenori], Koike, H.[Hideki], Arakawa, K.[Kenichi],
Online anomal movement detection based on unsupervised incremental learning,
ICPR08(1-4).
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

Zhao, D.L.[De-Li], Lin, Z.C.[Zhou-Chen], Tang, X.[Xiaoou],
Classification via semi-Riemannian spaces,
CVPR08(1-8).
IEEE DOI 0806
BibRef
Earlier:
Contextual Distance for Data Perception,
ICCV07(1-8).
IEEE DOI 0710
Context from nearest neighbors. BibRef

Liu, J.G.[Jin-Gen], Shah, M.[Mubarak],
Scene Modeling Using Co-Clustering,
ICCV07(1-7).
IEEE DOI 0710
Bag of Visterms (BOV). Group by similar concept. 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., 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 .


Last update:Nov 12, 2018 at 11:26:54