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Author Jia, Jianhua ♦ Liu, Bingxiang ♦ Jiao, Licheng
Source SpringerLink
Content type Text
Publisher SP Higher Education Press
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword spectral clustering (SC) ♦ Nyström approximation ♦ centralized logcontrast transform ♦ principal component analysis (PCA) ♦ ensemble learning ♦ Computer Science
Abstract An unsupervised learning algorithm, named soft spectral clustering ensemble (SSCE), is proposed in this paper. Until now many proposed ensemble algorithms cannot be used on image data, even images of a mere 256 × 256 pixels are too expensive in computational cost and storage. The proposed method is suitable for performing image segmentation and can, to some degree, solve some open problems of spectral clustering (SC). In this paper, a random scaling parameter and Nyström approximation are applied to generate the individual spectral clusters for ensemble learning. We slightly modify the standard SC algorithm to aquire a soft partition and then map it via a centralized logcontrast transform to relax the constraint of probability data, the sum of which is one. All mapped data are concatenated to form the new features for each instance. Principal component analysis (PCA) is used to reduce the dimension of the new features. The final aggregated result can be achieved by clustering dimension-reduced data. Experimental results, on UCI data and different image types, show that the proposed algorithm is more efficient compared with some existing consensus functions.
ISSN 16737350
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-10-28
Publisher Institution Chinese Universities
Publisher Place Heidelberg
e-ISSN 16737466
Journal Frontiers of Computer Science in China
Volume Number 5
Issue Number 1
Page Count 13
Starting Page 66
Ending Page 78


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Source: SpringerLink