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Algorithms for non-negative matrix factorization

D Seung, +1 more
- Vol. 13, pp 556-562
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The article was published on 2001-01-01 and is currently open access. It has received 5015 citations till now. The article focuses on the topics: Non-negative matrix factorization.

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Journal ArticleDOI

Analysis of the small RNA transcriptional response in multidrug-resistant Staphylococcus aureus after antimicrobial exposure

TL;DR: A large sRNA repertoire is defined in epidemic ST239 MRSA and it is shown for the first time that a subset of sRNAs are part of a coordinated transcriptional response to specific antimicrobial exposures in S. aureus.
Journal ArticleDOI

Direction of Arrival Based Spatial Covariance Model for Blind Sound Source Separation

TL;DR: The proposed SCM model is combined with a linear model for magnitudes and the parameter estimation is formulated in a complex-valued non-negative matrix factorization (CNMF) framework and is shown to exceed the performance of existing state of the art separation methods with two sources when evaluated by objective separation quality metrics.
Journal ArticleDOI

Discovering semantic features in the literature: a foundation for building functional associations

TL;DR: A method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents based on non-negative matrix factorization (NMF), a machine-learning algorithm capable of identifying local patterns that characterize a subset of the data.
Book ChapterDOI

Tensor sparse coding for region covariances

TL;DR: This paper proposes a novel approach for sparse representation of positive definite matrices, where vectorization would have destroyed the inherent structure of the data.
Journal ArticleDOI

Clustering in extreme learning machine feature space

TL;DR: Experiments show that the proposed ELM kMeans algorithm and ELM NMF (nonnegative matrix factorization) clustering can get better clustering results than the corresponding Mercer kernel based methods and the traditional algorithms using the original data.
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