A Sparse PCA Approach to Clustering

02/16/2016

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We discuss a clustering method for Gaussian mixture model based on the sparse principal component analysis (SPCA) method and compare it with the IF-PCA method. We also discuss the dependent case where the covariance matrix Σ is not necessarily diagonal.

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A Sparse PCA Approach to Clustering

Estimation of Gaussian Bi-Clusters with General Block-Diagonal Covariance Matrix and Applications

Sparse PCA on fixed-rank matrices

Covariance matrix preparation for quantum principal component analysis

Probing the Intra-Component Correlations within Fisher Vector for Material Classification

Extended Two-Dimensional PCA for Efficient Face Representation and Recognition

Comparison of Clustering Algorithms for Statistical Features of Vibration Data Sets

Outlier Detection In Large-scale Traffic Data By Naïve Bayes Method and Gaussian Mixture Model Method

A Sparse PCA Approach to Clustering

Related Research

Estimation of Gaussian Bi-Clusters with General Block-Diagonal Covariance Matrix and Applications

Sparse PCA on fixed-rank matrices

Covariance matrix preparation for quantum principal component analysis

Probing the Intra-Component Correlations within Fisher Vector for Material Classification

Extended Two-Dimensional PCA for Efficient Face Representation and Recognition

Comparison of Clustering Algorithms for Statistical Features of Vibration Data Sets

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