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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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Proceedings Article

Unfolding temporal dynamics: predicting social media popularity using multi-scale temporal decomposition

TL;DR: To predict photo popularity, a novel framework named Multi-scale Temporal Decomposition (MTD) is proposed, which decomposes the popularity matrix in latent spaces based on contextual associations and models time-sensitive context on different time scales, which is beneficial to automatically learn temporal patterns.
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Image retagging

TL;DR: This paper proposes a social image "retagging" scheme that aims at assigning images with better content descriptors and shows the remarkable performance improvements brought by retagging via two applications, i.e., tag-based search and automatic annotation.
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Gaussian Processes for Underdetermined Source Separation

TL;DR: A general formulation of underdetermined source separation as a problem involving GP regression is proposed and framing as a GP approximation is introduced to make the GP models tractable for very large signals and it is shown that computations for regularly sampled and locally stationary GPs can be done very efficiently in the frequency domain.
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LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates

TL;DR: A new algorithm, least squares non-negative matrix factorization, LS-NMF, is created, which integrates uncertainty measurements of gene expression data into NMF updating rules and significantly improves the power of the NMF technique.
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