SICA: subjectively interesting component analysis

DOI10.1007/s10618-018-0558-xzbMath1416.62327OpenAlexW2792807660WikidataQ130140631 ScholiaQ130140631MaRDI QIDQ1741396

Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie

Publication date: 3 May 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-018-0558-x

zbMATH Keywords

information theorydimensionality reductionsubjective interestingnessexploratory data miningforsiedSICAsubjectively interesting component analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• Manopt
• LMNN

Cites Work

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• The self-organizing map
• Introductory lectures on convex optimization. A basic course.
• On maximum entropy characterization of Pearson's type II and VII multivariate distributions
• Principal component analysis.
• A statistical significance testing approach to mining the most informative set of patterns
• Trace optimization and eigenproblems in dimension reduction methods
• Manopt, a Matlab toolbox for optimization on manifolds
• A Projection Pursuit Algorithm for Exploratory Data Analysis
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Multivariate T-Distributions and Their Applications
• RELATIONS BETWEEN TWO SETS OF VARIATES

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