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Robust Principal Component Analysis by Reverse Iterative Linear Programming

Authors: 

Andrea Visentin, Steven Prestwich, S. Armagan Tarim

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Principal Components Analysis (PCA) is a data analysis technique widely used in dimensionality reduction. It extracts a small number of orthonormal vectors that explain most of the variation in a dataset, which are called the Principal Components. Conventional PCA is sensitive to outliers because it is based on the L2-norm, so to improve robustness several algorithms based on the L1-norm have been introduced in the literature. We present a new algorithm for robust L1- norm PCA that computes components iteratively in reverse, using a new heuristic based on Linear Programming. This solution is focused on finding the projection that minimizes the variance of the projected points. It has only one parameter to tune, making it simple to use. On common benchmarks it performs competitively compared to other methods.
Conference Name: 
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Digital Object Identifer (DOI): 
10.1007/978-3-319-46227-1_37
Publication Date: 
04/09/2016
Volume: 
9852
Pages: 
593-605
Conference Location: 
Italy
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
No