کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
411807 679589 2015 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Sparse kernel entropy component analysis for dimensionality reduction of biomedical data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Sparse kernel entropy component analysis for dimensionality reduction of biomedical data
چکیده انگلیسی


• We propose a sparse kernel entropy component analysis (SKECA) algorithm.
• The SKECA algorithm is based on the divide-and-conquer method.
• The SKECA algorithm is applied to the dimensionality reduction of biomedical data.
• Experimental results show that the SKECA outperforms other conventional algorithms.

Dimensionality reduction is ubiquitous in biomedical applications. A newly proposed spectral dimensionality reduction method, named kernel entropy component analysis (KECA), can reveal the structure related to Renyi entropy of an input space data set. However, each principal component in the Hilbert space depends on all training samples in KECA, causing degraded performance. To overcome this drawback, a sparse KECA (SKECA) algorithm based on a recursive divide-and-conquer (DC) method is proposed in this work. The original large and complex problem of KECA is decomposed into a series of small and simple sub-problems, and then they are solved recursively. The performance of SKECA is evaluated on four biomedical datasets, and compared with KECA, principal component analysis (PCA), kernel PCA (KPCA), sparse PCA and sparse KPCA. Experimental results indicate that the SKECA outperforms conventional dimensionality reduction algorithms, even for high order dimensional features. It suggests that SKECA is potentially applicable to biomedical data processing.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 168, 30 November 2015, Pages 930–940
نویسندگان
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