Nonnegative matrix factorization (NMF) is widely used in data mining and machine learning fields. However, many data contain noises and outliers. Thus a robust version of NMF is needed. In this paper, we propose
Observation weights, specified as a nonnegative vector. Weights has length n, where n is the number of rows of X. The lasso function scales Weights to sum to 1. Data Types: single | double Output Arguments collapse all B— Fitted coefficients numeric matrix Fitted coefficients, returned as ...
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Deep matrix factorization methods can automatically learn the hidden representation of high dimensional data. However, they neglect the intrinsic geometric structure information of data. In this paper, we propose a Deep Semi-Nonnegative Matrix Factorization with Elastic Preserving (Deep Semi-NMF-EP) ...
Elastic-netRegularizationWeighted featuresClusteringPattern Analysis and Applications - In unsupervised learning, symmetric nonnegative matrix factorization (NMF) has proven its efficacy for various clustering tasks in recent years, considering both...
nonnegative matrix factorization (NMF) methodinsectodor approximationThe temperature dependence of the three independent elastic constants of antiferroelectric lead zirconate single crystals was determined in the cubic, paraelectric phase by Brillouin light scattering spectroscopy. Two longitudinal elastic moduli ...
Result: It is demonstrated that ENRSR gains more accurate and robust performance compared to the other 6 competing algorithms (K-means, Hierarchical Clustering, Expectation Maximization, Nonnegative Matrix Factorization, Support Vector Machine and Random Forest) in predicting cancer subtypes both on ...