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» Self-Paced Learning for Matrix Factorization
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ICML
2010
IEEE
13 years 8 months ago
A Simple Algorithm for Nuclear Norm Regularized Problems
Optimization problems with a nuclear norm regularization, such as e.g. low norm matrix factorizations, have seen many applications recently. We propose a new approximation algorit...
Martin Jaggi, Marek Sulovský
CVPR
2001
IEEE
14 years 9 months ago
Learning Representative Local Features for Face Detection
This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are ...
Xiangrong Chen, Lie Gu, Stan Z. Li, HongJiang Zhan...
CVPR
2001
IEEE
14 years 9 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...
ICCV
2005
IEEE
14 years 9 months ago
Learning Non-Negative Sparse Image Codes by Convex Programming
Example-based learning of codes that statistically encode general image classes is of vital importance for computational vision. Recently, non-negative matrix factorization (NMF) ...
Christoph Schnörr, Matthias Heiler
KDD
2010
ACM
286views Data Mining» more  KDD 2010»
13 years 5 months ago
Nonnegative shared subspace learning and its application to social media retrieval
Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can ser...
Sunil Kumar Gupta, Dinh Q. Phung, Brett Adams, Tru...