@inbook{0188cf78b249442683a20fa3271cad73,
title = "Robust principal component analysis by reverse iterative linear programming",
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. The data and software related to this paper are available at https://github. com/visentin-insight/L1-PCAhp.",
keywords = "L1-norm, Linear programming, Principal components analysis, Robust",
author = "Andrea Visentin and Steven Prestwich and \{Armagan Tarim\}, S.",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 ; Conference date: 19-09-2016 Through 23-09-2016",
year = "2016",
doi = "10.1007/978-3-319-46227-1\_37",
language = "English",
isbn = "9783319462264",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "593--605",
editor = "Niels Landwehr and Jilles Giuseppe and Giuseppe Manco and Paolo Frasconi",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings",
address = "Germany",
}