This paper proposes a method for supervised classification using Low-Rank Representation of transposed data. Recent papers have suggested that low rank representation of transposed data may be useful for feature extraction. We develop an algorithm called TLRRC for supervised classification using transposed data and demonstrate that its performance is competitive with state-of-the-art classification methods.
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