Title :
Topological local principal component analysis
Author :
Liu, Zhi-Yong ; Xu, Lei
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, China
Abstract :
We propose a topological local principal component analysis (PCA) in help of Kohonen´s self-organizing maps (SOM). The topological local PCA describes one cluster by one neuron such that it is capable of exploiting both the global topological structure and each local cluster structure. We also investigate a newly proposed self-organizing strategy that can enhance the learning speed, as well as an alternative Stiefel manifold based algorithm to ensure the orthonormality constraint of the local PCA.
Keywords :
Gaussian distribution; principal component analysis; self-organising feature maps; topology; unsupervised learning; Gaussian mixture model; Kohonen self-organizing maps; Stiefel manifold; global topological structure; learning speed; local cluster structure; orthonormality constraint; topological local principal component analysis; Clustering algorithms; Computer science; Councils; Covariance matrix; Feature extraction; Iterative algorithms; Maximum likelihood estimation; Mean square error methods; Principal component analysis; Self organizing feature maps;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1202840
