ICML 2004 - PDF Preprint Papers

ICML 2004 Workshop on Statistical Network Analysis

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ICML
2004
IEEE

360views Machine Learning» more ICML 2004»

K-means clustering via principal component analysis

15 years 19 days ago

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Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...

Chris H. Q. Ding, Xiaofeng He

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Id	Views	Title	Status

1	360	K-means clustering via principal component analysis	claim
2	223	Boosting margin based distance functions for clustering	claim
3	214	Apprenticeship learning via inverse reinforcement learning	claim
4	207	Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm	claim
5	205	Learning with non-positive kernels	claim
6	203	Training conditional random fields via gradient tree boosting	claim
7	202	Optimising area under the ROC curve using gradient descent	claim
8	198	Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5	claim
9	197	Distribution kernels based on moments of counts	claim
10	193	Co-EM support vector learning	claim
11	189	Learning a kernel matrix for nonlinear dimensionality reduction	claim
12	187	Automated hierarchical mixtures of probabilistic principal component analyzers	claim
13	185	A kernel view of the dimensionality reduction of manifolds	claim
14	184	Model selection via the AUC	claim
15	180	Variational methods for the Dirichlet process	claim
16	178	A fast iterative algorithm for fisher discriminant using heterogeneous kernels	claim
17	172	Large margin hierarchical classification	claim
18	171	Solving cluster ensemble problems by bipartite graph partitioning	claim
19	171	Robust feature induction for support vector machines	claim
20	169	Active learning using pre-clustering	claim
21	167	Bellman goes relational	claim
22	167	Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data	claim
23	166	The Bayesian backfitting relevance vector machine	claim
24	164	Estimating replicability of classifier learning experiments	claim
25	163	Multi-task feature and kernel selection for SVMs	claim
26	162	Generative modeling for continuous non-linearly embedded visual inference	claim
27	162	Learning and evaluating classifiers under sample selection bias	claim
28	161	Using relative novelty to identify useful temporal abstractions in reinforcement learning	claim
29	159	A maximum entropy approach to species distribution modeling	claim
30	158	Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning	claim
31	156	Ensemble selection from libraries of models	claim
32	156	Learning Bayesian network classifiers by maximizing conditional likelihood	claim
33	156	Learning to fly by combining reinforcement learning with behavioural cloning	claim
34	155	Sparse cooperative Q-learning	claim
35	155	Hyperplane margin classifiers on the multinomial manifold	claim
36	155	Decentralized detection and classification using kernel methods	claim
37	154	Predictive automatic relevance determination by expectation propagation	claim
38	154	Gradient LASSO for feature selection	claim
39	151	Generalized low rank approximations of matrices	claim
40	150	Support vector machine learning for interdependent and structured output spaces	claim
41	147	The multiple multiplicative factor model for collaborative filtering	claim
42	146	Multiple kernel learning, conic duality, and the SMO algorithm	claim
43	146	Semi-supervised learning using randomized mincuts	claim
44	146	Dynamic abstraction in reinforcement learning via clustering	claim
45	146	Learning to cluster using local neighborhood structure	claim
46	145	Convergence of synchronous reinforcement learning with linear function approximation	claim
47	144	Learning large margin classifiers locally and globally	claim
48	143	Links between perceptrons, MLPs and SVMs	claim
49	143	Probabilistic score estimation with piecewise logistic regression	claim
50	142	Learning and discovery of predictive state representations in dynamical systems with reset	claim
51	141	Gaussian process classification for segmenting and annotating sequences	claim
52	140	Learning associative Markov networks	claim
53	138	Online and batch learning of pseudo-metrics	claim
54	136	Learning to track 3D human motion from silhouettes	claim
55	136	Boosting grammatical inference with confidence oracles	claim
56	136	Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model	claim
57	135	Communication complexity as a lower bound for learning in games	claim
58	135	A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning	claim
59	135	Locally linear metric adaptation for semi-supervised clustering	claim
60	134	Approximate inference by Markov chains on union spaces	claim
61	133	An information theoretic analysis of maximum likelihood mixture estimation for exponential families	claim
62	133	Unifying collaborative and content-based filtering	claim
63	133	Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs	claim
64	131	Kernel conditional random fields: representation and clique selection	claim
65	131	Active learning of label ranking functions	claim
66	131	Learning to learn with the informative vector machine	claim
67	130	Parameter space exploration with Gaussian process trees	claim
68	128	Learning random walk models for inducing word dependency distributions	claim
69	127	A needle in a haystack: local one-class optimization	claim
70	127	Decision trees with minimal costs	claim
71	125	Testing the significance of attribute interactions	claim
72	123	Learning low dimensional predictive representations	claim
73	122	A graphical model for protein secondary structure prediction	claim
74	122	Ensembles of nested dichotomies for multi-class problems	claim
75	122	Online learning of conditionally I.I.D. data	claim
76	120	Relational sequential inference with reliable observations	claim
77	120	Improving SVM accuracy by training on auxiliary data sources	claim
78	120	Utile distinction hidden Markov models	claim
79	119	Sequential skewing: an improved skewing algorithm	claim
80	118	A multiplicative up-propagation algorithm	claim
81	118	Leveraging the margin more carefully	claim
82	118	P3VI: a partitioned, prioritized, parallel value iterator	claim
83	118	Redundant feature elimination for multi-class problems	claim
84	117	Tractable learning of large Bayes net structures from sparse data	claim
85	117	A spatio-temporal extension to Isomap nonlinear dimension reduction	claim
86	116	Towards tight bounds for rule learning	claim
87	115	Integrating constraints and metric learning in semi-supervised clustering	claim
88	115	A MFoM learning approach to robust multiclass multi-label text categorization	claim
89	115	Margin based feature selection - theory and algorithms	claim
90	113	A hierarchical method for multi-class support vector machines	claim
91	110	A Monte Carlo analysis of ensemble classification	claim
92	110	Learning first-order rules from data with multiple parts: applications on mining chemical compound data	claim
93	109	Lookahead-based algorithms for anytime induction of decision trees	claim
94	108	Nonparametric classification with polynomial MPMC cascades	claim
95	107	C4.5 competence map: a phase transition-inspired approach	claim
96	101	Extensions of marginalized graph kernels	claim
97	96	SVM-based generalized multiple-instance learning via approximate box counting	claim
98	93	Interpolation-based Q-learning	claim
99	91	Bias and variance in value function estimation	claim

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