Time: 11:00 — 11:50, Monday, May 23, 2016
Place: 1131 kemper Hall
Abstract: Over the past few decades, a lot of attention has been drawn to large-scale streaming data analysis, where researchers are faced with huge amount of high-dimensional data acquired in a stream fashion. In this case, conventional algorithms that compute the result from scratch whenever a new data comes are highly inefficient. To handle this problem, we propose a new incremental regularized least squares algorithm that is applied to supervised dimensionality reduction of large-scale streaming data with focus on linear discriminant analysis. Experimental results on real-world data sets demonstrate
the effectiveness and efficiency of our algorithms.
Host: Zhaojun Bai
1131 Kemper Hall