Open AccessLocally principal component analysis based on L1‐norm maximisation
Author(s) -
Lin Guanyou,
Tang Nianzu,
Wang Haixian
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2013.0851
Subject(s) - principal component analysis , norm (philosophy) , outlier , curse of dimensionality , dimensionality reduction , locality , computer science , pattern recognition (psychology) , artificial intelligence , mathematics , linguistics , philosophy , political science , law
Locally principal component analysis (LPCA) is a popular method of dimensionality reduction, which takes locality of data points into account. In this study, by using the L1‐norm instead of the L2‐norm in LPCA, the authors introduce a new formulation of LPCA based on the L1‐norm maximisation, referred to as LPCA‐L1. Compared with the conventional L2‐norm LPCA, the proposed LPCA‐L1 approach is more robust to outliers. Experiments of classification and recognition on the UCI, Yale and ORL data sets confirm the effectiveness of the proposed method.