The equivalence between principal component analysis and nearest flat in the least square sense

DOI10.1007/s10957-014-0647-yzbMath1322.15005OpenAlexW1974898383MaRDI QIDQ493252

Nai-Yang Deng, Yuan-Hai Shao

Publication date: 3 September 2015

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10957-014-0647-y

zbMATH Keywords

principal component analysisunsupervised learningmachine learninglinear manifoldeigenvalue decompositionnearest \(q\)-flat

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Learning and adaptive systems in artificial intelligence (68T05) Eigenvalues, singular values, and eigenvectors (15A18)

Related Items (1)

MBLDA: a novel multiple between-class linear discriminant analysis

Cites Work

• Robust recovery of multiple subspaces by geometric \(l_{p}\) minimization
• Foundations of a multi-way spectral clustering framework for hybrid linear modeling
• Nearest \(q\)-flat to \(m\) points
• \(k\)-plane clustering
• Principal component analysis.
• Hybrid linear modeling via local best-fit flats
• A two-phase heuristic for the bottleneck \(k\)-hyperplane clustering problem

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