We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Inde...
Le Song, Alexander J. Smola, Arthur Gretton, Karst...
This paper explores a recently proposed and rarely reported subspace learning method, Spectral Regression Discriminant Analysis (SRDA) [1, 2], on silhouette based human action rec...
Clustering aims to find useful hidden structures in data. In this paper we present a new clustering algorithm that builds upon the consistency method (Zhou, et.al., 2003), a semi-...
Clustering on multi-type relational data has attracted more and more attention in recent years due to its high impact on various important applications, such as Web mining, e-comm...
Bo Long, Zhongfei (Mark) Zhang, Xiaoyun Wu, Philip...
—This paper proposes a feature extraction method for motor imagery brain–computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomeno...
Haihong Zhang, Zhang Yang Chin, Kai Keng Ang, Cunt...