An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation

Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data. However, NMF pays the same attention to all attributes of a data point, which inevitably leads to inaccurate representations. For example, in a human-face dataset, if an image contains a hat...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2023-09, Vol.34 (9), p.5381-5391
Hauptverfasser:	Wei, Jiao, Tong, Can, Wu, Bingxue, He, Qiang, Qi, Shouliang, Yao, Yudong, Teng, Yueyang
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Clustering Cost function Data points Datasets Dimensionality reduction Entropy entropy regularizer Factorization Feature extraction Lagrange multiplier low-dimensional representation Manifolds nonnegative matrix factorization (NMF) Principal component analysis Representations
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