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      Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection

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          Most cited references 36

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          Wrappers for feature subset selection

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            Robust Recovery of Subspace Structures by Low-Rank Representation

             ,  ,   (2012)
            In this work we address the subspace recovery problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible errors as well. To this end, we propose a novel method termed Low-Rank Representation (LRR), which seeks the lowest-rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary. It is shown that LRR well solves the subspace recovery problem: when the data is clean, we prove that LRR exactly captures the true subspace structures; for the data contaminated by outliers, we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for the data corrupted by arbitrary errors, LRR can also approximately recover the row space with theoretical guarantees. Since the subspace membership is provably determined by the row space, these further imply that LRR can perform robust subspace segmentation and error correction, in an efficient way.
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              Gene expression correlates of clinical prostate cancer behavior

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                Author and article information

                Journal
                IEEE Transactions on Knowledge and Data Engineering
                IEEE Trans. Knowl. Data Eng.
                Institute of Electrical and Electronics Engineers (IEEE)
                1041-4347
                March 1 2018
                March 1 2018
                : 30
                : 3
                : 517-529
                Article
                10.1109/TKDE.2017.2763618
                © 2018
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