Name
Title | ||
|---|---|---|
Robust local subspace learning by linear fuzzy clustering with Alternative c-Means criterion |
Abstract | ||
|---|---|---|
Alternative c-Means model is a method for robustifying cluster estimation, in which a modified distance measure instead of the conventional Euclidean distance is used based on the robust M-estimation concept. In this paper, Fuzzy c-Varieties (FCV), which learns local subspace (i.e., FCV achieves local principal component analysis), is extended to a robustified version by using Alternative c-Means criterion. In order to replace the least square measure with alternative c-Means criterion, the clustering criteria of distances between data samples and linear prototypes are calculated by the lower rank approximation concept. Because the proposed method can extract local principal components in a robust way based on an iterative optimization scheme with additional typicality weights in a pseudo-M-estimation procedure, robust subspace learning can be performed in local area and achieves the lower dimensional visualization by using local principal components. In numerical experiments, the robust feature of the proposed model and the local lower visualization using Iris data set are demonstrated. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SCIS-ISIS.2012.6505103 | SCIS&ISIS |
Keywords | Field | DocType |
approximation theory,fuzzy set theory,iterative methods,learning (artificial intelligence),optimisation,pattern clustering,euclidean distance,fcv,alternative c-means criterion,approximation concept,cluster estimation,distance measure,fuzzy c-variety,iris data visualization,iterative optimization scheme,least square measure,linear fuzzy clustering,pseudom-estimation procedure,robust m-estimation concept,robust subspace learning,fuzzy clustering,principal component analysis,robust clustering | Fuzzy clustering,Computer science,Artificial intelligence,Cluster analysis,Mathematical optimization,Subspace topology,Pattern recognition,Visualization,Fuzzy logic,Euclidean distance,Iris flower data set,Machine learning,Principal component analysis | Conference |
ISSN | ISBN | Citations |
2377-6870 | 978-1-4673-2742-8 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nakao, S. | 1 | 0 | 0.34 |
| K. Honda | 2 | 145 | 12.73 |
| Notsu, A. | 3 | 13 | 4.23 |
| Hidetomo Ichihashi | 4 | 370 | 72.85 |