IEEE Trans Image Process - General subspace learning with corrupted training data via graph embedding.

Tópicos

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Resumo

We address the following subspace learning problem: supposing we are given a set of labeled, corrupted training data points, how to learn the underlying subspace, which contains three components: an intrinsic subspace that captures certain desired properties of a data set, a penalty subspace that fits the undesired properties of the data, and an error container that models the gross corruptions possibly existing in the data. Given a set of data points, these three components can be learned by solving a nuclear norm regularized optimization problem, which is convex and can be efficiently solved in polynomial time. Using the method as a tool, we propose a new discriminant analysis (i.e., supervised subspace learning) algorithm called Corruptions Tolerant Discriminant Analysis (CTDA), in which the intrinsic subspace is used to capture the features with high within-class similarity, the penalty subspace takes the role of modeling the undesired features with high between-class similarity, and the error container takes charge of fitting the possible corruptions in the data. We show that CTDA can well handle the gross corruptions possibly existing in the training data, whereas previous linear discriminant analysis algorithms arguably fail in such a setting. Extensive experiments conducted on two benchmark human face data sets and one object recognition data set show that CTDA outperforms the related algorithms.

Resumo Limpo

address follow subspac learn problem suppos given set label corrupt train data point learn under subspac contain three compon intrins subspac captur certain desir properti data set penalti subspac fit undesir properti data error contain model gross corrupt possibl exist data given set data point three compon can learn solv nuclear norm regular optim problem convex can effici solv polynomi time use method tool propos new discrimin analysi ie supervis subspac learn algorithm call corrupt toler discrimin analysi ctda intrins subspac use captur featur high withinclass similar penalti subspac take role model undesir featur high betweenclass similar error contain take charg fit possibl corrupt data show ctda can well handl gross corrupt possibl exist train data wherea previous linear discrimin analysi algorithm arguabl fail set extens experi conduct two benchmark human face data set one object recognit data set show ctda outperform relat algorithm

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