Symmetric nonnegative matrix factorization: A systematic review
WS Chen, K **e, R Liu, B Pan - Neurocomputing, 2023 - Elsevier
In recent years, symmetric non-negative matrix factorization (SNMF), a variant of non-
negative matrix factorization (NMF), has emerged as a promising tool for data analysis. This …
negative matrix factorization (NMF), has emerged as a promising tool for data analysis. This …
Multiple incomplete views clustering via weighted nonnegative matrix factorization with regularization
With the advance of technology, data are often with multiple modalities or coming from
multiple sources. Multi-view clustering provides a natural way for generating clusters from …
multiple sources. Multi-view clustering provides a natural way for generating clusters from …
Robust structured nonnegative matrix factorization for image representation
Dimensionality reduction has attracted increasing attention, because high-dimensional data
have arisen naturally in numerous domains in recent years. As one popular dimensionality …
have arisen naturally in numerous domains in recent years. As one popular dimensionality …
Robust semi-supervised nonnegative matrix factorization for image clustering
Nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has
received increasing attention in various practical applications. However, most traditional …
received increasing attention in various practical applications. However, most traditional …
Unsupervised feature selection by self-paced learning regularization
Previous feature selection methods equivalently consider the samples to select important
features. However, the samples are often diverse. For example, the outliers should have …
features. However, the samples are often diverse. For example, the outliers should have …
Online robust non-negative dictionary learning for visual tracking
This paper studies the visual tracking problem in video sequences and presents a novel
robust sparse tracker under the particle filter framework. In particular, we propose an online …
robust sparse tracker under the particle filter framework. In particular, we propose an online …
A decision support system for supplier quality evaluation based on MCDM-aggregation and machine learning
Q Ma, H Li - Expert Systems with Applications, 2024 - Elsevier
Evaluating suppliers' quality performance is one of critical tasks of quality management
since it is directly related to quality assurance, improvement and development, especially for …
since it is directly related to quality assurance, improvement and development, especially for …
Self-paced nonnegative matrix factorization for hyperspectral unmixing
The presence of mixed pixels in the hyperspectral data makes unmixing to be a key step for
many applications. Unsupervised unmixing needs to estimate the number of endmembers …
many applications. Unsupervised unmixing needs to estimate the number of endmembers …
Truncated Cauchy non-negative matrix factorization
Non-negative matrix factorization (NMF) minimizes the euclidean distance between the data
matrix and its low rank approximation, and it fails when applied to corrupted data because …
matrix and its low rank approximation, and it fails when applied to corrupted data because …
Speech enhancement under low SNR conditions via noise estimation using sparse and low-rank NMF with Kullback–Leibler divergence
A key stage in speech enhancement is noise estimation which usually requires prior models
for speech or noise or both. However, prior models can sometimes be difficult to obtain. In …
for speech or noise or both. However, prior models can sometimes be difficult to obtain. In …