Fast and Robust Recursive Algorithmsfor Separable Nonnegative Matrix Factorization
نویسندگان
چکیده
منابع مشابه
Robust near-separable nonnegative matrix factorization using linear optimization
Nonnegative matrix factorization (NMF) has been shown recently to be tractable under the separability assumption, which amounts for the columns of the input data matrix to belong to the convex cone generated by a small number of columns. Bittorf, Recht, Ré and Tropp (‘Factoring nonnegative matrices with linear programs’, NIPS 2012) proposed a linear programming (LP) model, referred to as HottTo...
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Nonnegative matrix factorization (NMF) under the separability assumption can provably be solved efficiently, even in the presence of noise, and has been shown to be a powerful technique in document classification and hyperspectral unmixing. This problem is referred to as near-separable NMF and requires that there exists a cone spanned by a small subset of the columns of the input nonnegative ma...
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This paper presents a novel robust digital image watermarking scheme using subsampling and nonnegative matrix factorization. Firstly, subsampling is used to construct a subimage sequence. Then, based on the column similarity of the subimage sequence, nonnegative matrix factorization (NMF) is applied to decompose the sequence. A Gaussian pseudo-random watermark sequence is embedded in the factor...
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Article history: Received 16 September 2007 Received in revised form 1 July 2008 Accepted 16 September 2008 Available online 5 November 2008
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2014
ISSN: 0162-8828,2160-9292
DOI: 10.1109/tpami.2013.226