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Found 3 papers, 1 papers with code
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow
Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction.
Feature Grouping and Sparse Principal Component Analysis with Truncated Regularization
In this paper, we consider a new variant for principal component analysis (PCA), aiming to capture the grouping and/or sparse structures of factor loadings simultaneously.
Robust Matrix Factorization with Grouping Effect
Compared with existing algorithms, the proposed GRMF can automatically learn the grouping structure and sparsity in MF without prior knowledge, by introducing a naturally adjustable non-convex regularization to achieve simultaneous sparsity and grouping effect.
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