Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or la...
Jun Yan, Benyu Zhang, Shuicheng Yan, Qiang Yang, H...
Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn ...
Lei Li, James McCann, Nancy S. Pollard, Christos F...
This paper is concerned with reducing communication costs when executing distributed user tasks in a sensor network. We take a service-oriented abstraction of sensor networks, whe...
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Sequence segmentation is a central problem in the analysis of sequential and time-series data. In this paper we introduce and we study a novel variation to the segmentation proble...