A Flexible and Effective Linearization Method for Subspace Learning

In the past decades, a large number of subspace learning or dimension reduction methods [2, 16, 20, 32, 34, 37, 44] have been proposed. Principal component analysis (PCA) [32] pursues the directions of maximum variance for optimal reconstruction. Linear discriminant analysis (LDA) [2], as a supervised algorithm, aims to maximize the inter-class scatter and at the same time minimize the intra-class scatter. Due to utilization of label information, LDA is experimentally reported to outperform PCA for face recognition, when sufficient labeled face images are provided [2].

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