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ICML 2007 Workshop on Statistical Network Analysis

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ICML
2007
IEEE

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Discriminant analysis in correlation similarity measure space

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Correlation is one of the most widely used similarity measures in machine learning like Euclidean and Mahalanobis distances. However, compared with proposed numerous discriminant ...

Yong Ma, Shihong Lao, Erina Takikawa, Masato Kawad...

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Id	Views	Title	Status

1	384	Discriminant analysis in correlation similarity measure space	claim
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17	184	Non-isometric manifold learning: analysis and an algorithm	claim
18	184	Adaptive mesh compression in 3D computer graphics using multiscale manifold learning	claim
19	180	Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation	claim
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49	160	Transductive regression piloted by inter-manifold relations	claim
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66	146	Mixtures of hierarchical topics with Pachinko allocation	claim
67	146	Beamforming using the relevance vector machine	claim
68	145	Sample compression bounds for decision trees	claim
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71	142	Discriminative learning for differing training and test distributions	claim
72	141	Minimum reference set based feature selection for small sample classifications	claim
73	141	Exponentiated gradient algorithms for log-linear structured prediction	claim
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75	141	Reinforcement learning by reward-weighted regression for operational space control	claim
76	141	On the role of tracking in stationary environments	claim
77	139	Learning state-action basis functions for hierarchical MDPs	claim
78	139	Most likely heteroscedastic Gaussian process regression	claim
79	139	Multi-armed bandit problems with dependent arms	claim
80	138	On learning with dissimilarity functions	claim
81	137	Regression on manifolds using kernel dimension reduction	claim
82	136	On the value of pairwise constraints in classification and consistency	claim
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86	134	More efficiency in multiple kernel learning	claim
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89	132	Local learning projections	claim
90	132	Sparse probabilistic classifiers	claim
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111	119	Relational clustering by symmetric convex coding	claim
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139	104	Learning for efficient retrieval of structured data with noisy queries	claim
140	102	Feature selection in a kernel space	claim
141	102	An empirical evaluation of deep architectures on problems with many factors of variation	claim
142	101	Intractability and clustering with constraints	claim
143	98	Support cluster machine	claim
144	97	On the relation between multi-instance learning and semi-supervised learning	claim
145	97	Two-view feature generation model for semi-supervised learning	claim
146	95	Revisiting probabilistic models for clustering with pair-wise constraints	claim
147	94	Supervised clustering of streaming data for email batch detection	claim
148	88	Multiple instance learning for sparse positive bags	claim

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