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 developed 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 realworld data sets demonstrate the effectiveness and efficiency of our algorithm.

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