2012/07/02 by Peratham Wiriyathammabhum, Wiriyathammabhum, Peratham, Boonserm Kijsirikul +1 · 2 citations
Chemistry · Computer Science · Mathematics · #Advanced Statistical Methods and Models #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Covariance #Covariance matrix #Data mining #Estimator #FOS: Computer and information sciences #Face and Expression Recognition #Kernel (algebra) #Kernel method #Kernel principal component analysis #Mathematics #Outlier #Pattern recognition (psychology) #Principal component analysis #Robust principal component analysis #Robust statistics #Robustness (evolution) #Spectroscopy and Chemometric Analyses #Statistic #Statistics #Support vector machine #cs.AI
paper · pdf · doi:10.48550/arxiv.1207.0403
published in arXiv (Cornell University) (Cornell University) · In Proc. of the International Joint Conference on Computer Science and Software Engineering (JCSSE) 2009
arxiv created 2012/07/02 · openalex publication_date 2012/07/02 · arxiv updated 2012/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Principal Component Analysis (PCA) finds a linear mapping and maximizes the variance of the data which makes PCA sensitive to outliers and may cause wrong eigendirection. In this paper, we propose techniques to solve this problem; we use the data-centering method and reestimate the covariance matrix using robust statistic techniques such as median, robust scaling which is a booster to data-centering and Huber M-estimator which measures the presentation of outliers and reweight them with small values. The results on several real world data sets show that our proposed method handles outliers and gains better results than the original PCA and provides the same accuracy with lower computation cost than the Kernel PCA using the polynomial kernel in classification tasks.