Journal ArticleDOI
Sparse kernel entropy component analysis for dimensionality reduction of biomedical data
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TLDR
A sparse KECA (SKECA) algorithm based on a recursive divide-and-conquer (DC) method that outperforms conventional dimensionality reduction algorithms, even for high order dimensional features, suggests that SKECA is potentially applicable to biomedical data processing.About:
This article is published in Neurocomputing.The article was published on 2015-11-30. It has received 31 citations till now. The article focuses on the topics: Sparse PCA & Kernel principal component analysis.read more
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Journal ArticleDOI
Fused Sparse Network Learning for Longitudinal Analysis of Mild Cognitive Impairment
TL;DR: This article utilizes multiple rs-fMRI time-point to identify early MCI (EMCI) and lateMCI (LMCI), by integrating the fused sparse network (FSN) model with parameter-free centralized (PFC) learning and fused by a similarity network fusion (SNF) method.
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Projective parameter transfer based sparse multiple empirical kernel learning Machine for diagnosis of brain disease
TL;DR: A novel projective model (PM) based sparse MEKLM(PM-SMEKLM) algorithm to learn a cross-domain transformation by PM in way of the parameter-based TL, and then apply it to the neuroimaging-based CAD for brain diseases.
Journal ArticleDOI
Kernel-based dimensionality reduction using Renyi's α-entropy measures of similarity
TL;DR: KEDR measures the embedding quality that is based on stochastic neighborhood preservation, involving a Gram matrix estimation of Renyi's α-entropy, resulting in an improved the preservation of both the local and global data structures.
Journal ArticleDOI
Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings
TL;DR: This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings which makes full use of the labeled information and introduces a weight strategy in the feature extraction.
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A Diagnostics Method for Analog Circuits Based on Improved Kernel Entropy Component Analysis
TL;DR: A multiplicative bias correction method of the entropy estimation and a method for fault diagnosis of analog circuits based on the combination of improved KECA and extreme learning machine (ELM) is presented.
References
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Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
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On Estimation of a Probability Density Function and Mode
TL;DR: In this paper, the problem of the estimation of a probability density function and of determining the mode of the probability function is discussed. Only estimates which are consistent and asymptotically normal are constructed.
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Robust principal component analysis
TL;DR: In this paper, the authors prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the e1 norm.
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A review of feature selection techniques in bioinformatics
TL;DR: A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
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Sparse Principal Component Analysis
TL;DR: This work introduces a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings and shows that PCA can be formulated as a regression-type optimization problem.