NDLI: Sparse kernel entropy component analysis for dimensionality reduction of neuroimaging data

Content Provider	IEEE Xplore Digital Library
Author	Qikun Jiang Jun Shi
Copyright Year	2014
Description	Author affiliation: Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China (Qikun Jiang; Jun Shi)
Abstract	The neuroimaging data typically has extremely high dimensions. Therefore, dimensionality reduction is commonly used to extract discriminative features. Kernel entropy component analysis (KECA) is a newly developed data transformation method, where the key idea is to preserve the most estimated Renyi entropy of the input space data set via a kernel-based estimator. Despite its good performance, KECA still suffers from the problem of low computational efficiency for large-scale data. In this paper, we proposed a sparse KECA (SKECA) algorithm with the recursive divide-and-conquer based solution, and then applied it to perform dimensionality reduction of neuroimaging data for classification of the Alzheimer's disease (AD). We compared the SKECA with KECA, principal component analysis (PCA), kernel PCA (KPCA) and sparse KPCA. The experimental results indicate that the proposed SKECA has most superior performance to all other algorithms when extracting discriminative features from neuroimaging data for AD classification.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	3366
Ending Page	3369
File Size	710912
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944344
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Kernel Feature extraction Entropy Classification algorithms Neuroimaging Diseases
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Health Informatics Signal Processing Biomedical Engineering

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