Advertisement
Published in:
2011 | OriginalPaper | Chapter
Numerical Evaluation of Clustering Methods with Kernel PCA
Authors : Hiromi Miyajima, Noritaka Shigei, Tomiyuki Shiiba
Published in: Convergence and Hybrid Information Technology
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Kernel methods are ones that, by replacing the inner product with positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. The clustering methods using kernel function (kernel clustering methods) are superior in accuracy to the conventional ones such as K-Means (KM) and Neural-Gas (NG). But, it seems that kernel clustering methods do not always show sufficient ability of clustering. One method to improve them is to find expression of approximation for data in the feature space. In this paper, we introduce the kernel PCA and apply it to clustering methods as KM and NG. Further, we apply it to derived kernel method, which means twice application of kernel functions. The simulation results show that the proposed clustering methods are superior in terms of accuracy to the conventional methods.