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AAAI
2011

Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions

12 years 3 months ago
Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions
Automated feature discovery is a fundamental problem in machine learning. Although classical feature discovery methods do not guarantee optimal solutions in general, it has been recently noted that certain subspace learning and sparse coding problems can be solved efficiently, provided the number of features is not restricted a priori. We provide an extended characterization of this optimality result and describe the nature of the solutions under an expanded set of practical contexts. In particular, we apply the framework to a semisupervised learning problem, and demonstrate that feature discovery can co-occur with input reconstruction and supervised training while still admitting globally optimal solutions. A comparison to existing semi-supervised feature discovery methods shows improved generalization and efficiency.
Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong H
Added 12 Dec 2011
Updated 12 Dec 2011
Type Journal
Year 2011
Where AAAI
Authors Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans
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