ArticleSensors (Basel, Switzerland)2020
An Efficient Orthonormalization-Free Approach for Sparse Dictionary Learning and Dual Principal Component Pursuit.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Data, Signal and Image Processing and Applications in Sensors.Sensors (Basel, Switzerland) · 2021Article
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Abstract
Sparse dictionary learning (SDL) is a classic representation learning method and has been widely used in data analysis. Recently, the ℓ m -norm ( m ≥ 3 , m ∈ N ) maximization has been proposed to solve SDL, which reshapes the problem to an optimization problem with orthogonality constraints. In this paper, we first propose an ℓ m -norm maximization model for solving dual principal component pursuit (DPCP) based on the similarities between DPCP and SDL. Then, we propose a smooth unconstrained exact penalty model and show its equivalence with the ℓ m -norm maximization model. Based on our penalty model, we develop an efficient first-order algorithm for solving our penalty model (PenNMF) and show its global convergence. Extensive experiments illustrate the high efficiency of PenNMF when compared with the other state-of-the-art algorithms on solving the ℓ m -norm maximization with orthogonality constraints.
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