Discover latent discriminant information for dimensionality reduction: non-negative sparseness preserving embedding

DOI10.1016/j.patcog.2011.10.024zbMath1231.68223OpenAlexW1979741427MaRDI QIDQ663379

Juan-Miguel Gracia

Publication date: 14 February 2012

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.10.024

zbMATH Keywords

feature extractiondimensionality reductionmanifold learningsparse representationnon-negative matrix factorization

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Learning and adaptive systems in artificial intelligence (68T05) Positive matrices and their generalizations; cones of matrices (15B48)

Related Items (1)

A classification-oriented dictionary learning model: explicitly learning the particularity and commonality across categories

Uses Software

• KPCA plus LDA
• L1-MAGIC
• CMU PIE

Cites Work

• Unnamed Item
• Sparsity preserving projections with applications to face recognition
• Algorithms and applications for approximate nonnegative matrix factorization
• Atomic Decomposition by Basis Pursuit
• Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
• Learning the parts of objects by non-negative matrix factorization

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