Image clusteringNonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has received increasing attention in various practical applications. However, most traditional NMF based algo
Robust nonnegative matrix factorization via ℓ1 norm regularization,” ArXiv preprint arXiv:1204.2311 - Shen, Si, et al. - 2012 () Citation Context ...lem. Robust NMF is a nonnegative variant of robust PCA [8] which has appeared in different forms in the literature. In [9], the ...
In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l(2,1)-norm and the non-negative constraints not only removes ...
(2011). Generalized alpha-beta di- vergences and their application to robust nonnegative matrix factorization. Entropy, 13:134-170.A. Cichocki, S. Cruces, and S.-I. Amari. General- ized alpha-beta divergences and their application to robust nonnegative matrix factorization. En- tropy, 13:...
Non-negative matrix factorization (NMF) and its variants have been widely employed in clustering and classification task. However, the existing methods do not consider robustness, adaptive graph learning and discrimination information at the same time. To solve this problem, a new nonnegative matrix ...
Many methods have been proposed recently for high-dimensional data representation to reduce the dimensionality of the data. Matrix Factorization (MF) as an
Robust self supervised symmetric nonnegative matrix factorization to the graph clustering ArticleOpen access01 March 2025 Degree difference: a simple measure to characterize structural heterogeneity in complex networks 07 December 2020 Introduction Almost everything in our daily life can be modeled as compl...
We used nonnegative matrix factorization to identify muscle synergies during postural recovery responses in human and to examine the functional significance of such synergies for hyper-gravity (1.75g) and hypo-gravity (0.25g). Electromyographic data were recorded from leg, trunk and arm muscles of ...
Nonnegative matrix factorizationCo-clustering is to group features and samples simultaneously and has received increasing attention in data mining and machine learning, particularly in text document categorization and gene expression. In this paper, two effective co-clustering algorithms are proposed to ...
BMC Genomics (2024) 25:885 https://doi.org/10.1186/s12864-024-10729-w BMC Genomics RESEARCH Open Access SPLHRNMTF: robust orthogonal non‑negative matrix tri‑factorization with self‑paced learning and dual hypergraph regularization for predicting miRNA‑disease associations Dong ...