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» Learning Parts-Based Representations of Data
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CVPR
2008
IEEE
15 years 5 hour ago
Joint learning and dictionary construction for pattern recognition
We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier p...
Duc-Son Pham, Svetha Venkatesh
IJPRAI
2008
144views more  IJPRAI 2008»
14 years 12 months ago
Unsupervised Learning of a Hierarchy of Topological Maps Using Omnidirectional Images
unsupervised construction of topological maps, which provide an abstraction of the environment in terms of visual aspects. An unsupervised clustering algorithm is used to represent...
Ales Stimec, Matjaz Jogan, Ales Leonardis
TSP
2010
14 years 6 months ago
Double sparsity: learning sparse dictionaries for sparse signal approximation
Abstract--An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of ...
Ron Rubinstein, Michael Zibulevsky, Michael Elad
ALT
2003
Springer
15 years 3 months ago
Efficiently Learning the Metric with Side-Information
Abstract. A crucial problem in machine learning is to choose an appropriate representation of data, in a way that emphasizes the relations we are interested in. In many cases this ...
Tijl De Bie, Michinari Momma, Nello Cristianini
PAMI
2008
182views more  PAMI 2008»
14 years 11 months ago
Gaussian Process Dynamical Models for Human Motion
We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motio...
Jack M. Wang, David J. Fleet, Aaron Hertzmann