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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

Subclass discriminant Nonnegative Matrix Factorization for facial image analysis

TL;DR: The proposed method incorporates appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space, while taking into account subclass information.
Proceedings Article

Fusion of Similarity Data in Clustering

TL;DR: This work presents an approach to utilize multiple information sources in the form of similarity data for unsupervised learning, and employs a stability-based approach to ensure the selection of the most self-consistent hypothesis.
Journal ArticleDOI

Fast nonnegative matrix factorization and its application for protein fold recognition

TL;DR: Nonnegative matrix factorization in combination with three nearest-neighbor classifiers for protein fold recognition is explored, with gains that can reach more than 4%, compared to the classification in the original, high-dimensional space.
Journal ArticleDOI

Neural variational matrix factorization for collaborative filtering in recommendation systems

TL;DR: A novel deep generative model that incorporates side information of both users and items to capture better latent representations of them for more effective collaborative-filtering recommendation and significantly outperforms the state-of-the-art methods on recommendation accuracy measured by Hit Ratio and Normalized Discounted Cumulative Gain respectively.
Journal ArticleDOI

Compact and Discriminative Descriptor Inference Using Multi-Cues

TL;DR: A descriptor learning framework to optimize the descriptors at the source by learning a projection from multiple descriptors' spaces to a new Euclidean space to enhance the descriptive and discriminative ability from multiple cues.
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