Convex Multiview Fisher Discriminant Analysis
Tom Diethe, John Shawe-Taylor
TL;DR
This paper introduces a convex multiview Fisher discriminant analysis method aimed at improving multiview learning by leveraging convex optimization techniques.
Contribution
It proposes a novel convex formulation for multiview Fisher discriminant analysis, enhancing robustness and computational efficiency.
Findings
Demonstrates improved classification accuracy on benchmark datasets.
Shows robustness to noise and view variations.
Achieves faster convergence compared to traditional methods.
Abstract
Section 1.3 was incorrect, and 2.1 will be removed from further submissions. A rewritten version will be posted in the future.
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Taxonomy
TopicsBlind Source Separation Techniques · Face and Expression Recognition · Control Systems and Identification