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CVPR
2001
IEEE
16 years 8 months ago
Learning Spatially Localized, Parts-Based Representation
In this paper, we propose a novel method, called local nonnegative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual pat...
Stan Z. Li, XinWen Hou, HongJiang Zhang, QianSheng...
ACCV
2006
Springer
15 years 10 months ago
Multiple Similarities Based Kernel Subspace Learning for Image Classification
Abstract. In this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in...
Wang Yan, Qingshan Liu, Hanqing Lu, Songde Ma
ICML
2003
IEEE
16 years 7 months ago
Relativized Options: Choosing the Right Transformation
Relativized options combine model minimization methods and a hierarchical reinforcement learning framework to derive compact reduced representations of a related family of tasks. ...
Balaraman Ravindran, Andrew G. Barto
IJCAI
1989
15 years 7 months ago
Noise-Tolerant Instance-Based Learning Algorithms
Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage requirements during supervised learning application...
David W. Aha, Dennis F. Kibler
ICIP
2004
IEEE
16 years 8 months ago
Simplified digital holographic reconstruction using statistical methods
For reconstructing a complex object wavefront from digital holograms, we propose a new penalized-likelihood approach based on the measurement statistics and edge-preserving regula...
Jeffrey A. Fessler, Saowapak Sotthivirat