We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and cluster...
An important aspect of clustering algorithms is whether the partitions constructed on finite samples converge to a useful clustering of the whole data space as the sample size inc...
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belk...
Abstract. In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from longterm observations of mo...
— Peer-to-peer computing, the harnessing of idle compute cycles throughout the Internet, offers exciting new research challenges in the converging domains of networking and distr...
Virginia Mary Lo, Daniel Zappala, Dayi Zhou, Yuhon...
High-performance computing (HPC) systems consume a significant amount of power, resulting in high operational costs, reduced reliability, and wasting of natural resources. Therefor...
Reza Zamani, Ahmad Afsahi, Ying Qian, V. Carl Hama...