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CORR
2007
Springer
167views Education» more  CORR 2007»
9 years 3 months ago
Optimal Solutions for Sparse Principal Component Analysis
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonze...
Alexandre d'Aspremont, Francis R. Bach, Laurent El...
CORR
2007
Springer
198views Education» more  CORR 2007»
9 years 3 months ago
Clustering and Feature Selection using Sparse Principal Component Analysis
In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combi...
Ronny Luss, Alexandre d'Aspremont
NIPS
2008
9 years 5 months ago
Sparse probabilistic projections
We present a generative model for performing sparse probabilistic projections, which includes sparse principal component analysis and sparse canonical correlation analysis as spec...
Cédric Archambeau, Francis Bach
ICML
2007
IEEE
10 years 4 months ago
Sparse eigen methods by D.C. programming
Eigenvalue problems are rampant in machine learning and statistics and appear in the context of classification, dimensionality reduction, etc. In this paper, we consider a cardina...
Bharath K. Sriperumbudur, David A. Torres, Gert R....
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