Please use this identifier to cite or link to this item: https://doi.org/10.1109/TPAMI.2008.154
Title: Correlation metric for generalized feature extraction
Authors: Fu, Y.
Yan, S. 
Huang, T.S.
Keywords: Correlation embedding analysis
Correlational principal component analysis
Face recognition
Feature extraction
Graph embedding
Issue Date: 2008
Source: Fu, Y., Yan, S., Huang, T.S. (2008). Correlation metric for generalized feature extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (12) : 2229-2235. ScholarBank@NUS Repository. https://doi.org/10.1109/TPAMI.2008.154
Abstract: Beyond conventional linear and kernel-based feature extraction, we propose in this paper the generalized feature extraction formulation based on the so-called Graph Embedding framework. Two novel correlation metric based algorithms are presented based on this formulation. Correlation Embedding Analysis (CEA), which incorporates both correlational mapping and discriminating analysis, boosts the discriminating power by mapping data from a high-dimensional hypersphere onto another low-dimensional hypersphere and preserving the intrinsic neighbor relations with local graph modeling. Correlational Principal Component Analysis (CPCA) generalizes the conventional Principal Component Analysis (PCA) algorithm to the case with data distributed on a high-dimensional hypersphere. Their advantages stem from two facts: 1) tailored to normalized data, which are often the outputs from the data preprocessing step, and 2) directly designed with correlation metric, which shows to be generally better than Euclidean distance for classification purpose. Extensive comparisons with existing algorithms on visual classification experiments demonstrate the effectiveness of the proposed methods. © 2008 IEEE.
Source Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/55455
ISSN: 01628828
DOI: 10.1109/TPAMI.2008.154
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