2012/03/05 by Brian McWilliams, McWilliams, Brian, Giovanni Montana +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Advanced Clustering Algorithms Research #FOS: Computer and information sciences #Face and Expression Recognition #Gene expression and cancer classification #Machine Learning (stat.ML) #stat.ML
paper · pdf · doi:10.48550/arxiv.1203.1065
arxiv created 2012/03/05 · openalex publication_date 2012/03/05 · arxiv updated 2012/03/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In several application domains, high-dimensional observations are collected and then analysed in search for naturally occurring data clusters which might provide further insights about the nature of the problem. In this paper we describe a new approach for partitioning such high-dimensional data. Our assumption is that, within each cluster, the data can be approximated well by a linear subspace estimated by means of a principal component analysis (PCA). The proposed algorithm, Predictive Subspace Clustering (PSC) partitions the data into clusters while simultaneously estimating cluster-wise PCA parameters. The algorithm minimises an objective function that depends upon a new measure of influence for PCA models. A penalised version of the algorithm is also described for carrying our simultaneous subspace clustering and variable selection. The convergence of PSC is discussed in detail, and extensive simulation results and comparisons to competing methods are presented. The comparative performance of PSC has been assessed on six real gene expression data sets for which PSC often provides state-of-art results.