Paper Info
Title
Penalty Constraints and Kernelization of M-Estimation Based Fuzzy C-Means
Abstract
A framework of M-estimation based fuzzy C-means clustering (MFCM) algorithm is proposed with iterative reweighted least squares (IRLS) algorithm, and penalty constraint and kernelization extensions of MFCM algorithms are also developed. Introducing penalty information to the object functions of MFCM algorithms, the spatially constrained fuzzy C-means (SFCM) is extended to penalty constraints MFCM algorithms(abbr. pMFCM).Substituting the Euclidean distance with kernel method, the MFCM and pMFCM algorithms are extended to kernelized MFCM (abbr. KMFCM) and kernelized pMFCM (abbr.pKMFCM) algorithms. The performances of MFCM, pMFCM, KMFCM and pKMFCM algorithms are evaluated in three tasks: pattern recognition on 10 standard data sets from UCI Machine Learning databases, noise image segmentation performances on a synthetic image, a magnetic resonance brain image (MRI), and image segmentation of a standard images from Berkeley Segmentation Dataset and Benchmark. The experimental results demonstrate the effectiveness of our proposed algorithms in pattern recognition and image segmentation.
Year
Venue
Field
2012
CoRR
Kernelization,Data set,Pattern recognition,Segmentation,Computer science,Euclidean distance,Fuzzy logic,Image segmentation,Artificial intelligence,Kernel method,Cluster analysis,Machine learning
DocType
Volume
Citations 
Journal
abs/1207.4417
0
PageRank 
References 
Authors
0.34
19
2
Name
Order
Citations
PageRank
Jingwei Liu12010.65
Meizhi Xu2131.74