Matrix factorization techniques have become pivotal in data mining, enabling the extraction of latent structures from large-scale data matrices. These methods decompose complex datasets into lower ...

Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix ...

In this paper, we propose a co-sparse non-negative matrix factorization framework to impose sparsity in both the coding matrix and the basis matrix. The co-sparsity is realized by limiting the total ...

EDGES is a spatially constrained non-negative matrix factorization (NMF) framework designed for gene expression denoising and prediction in spatial transcriptomic (ST) data.

Matrix factorization in numerical linear algebra (NLA) typically serves the purpose of restating some given problem in such a way that it can be solved more readily; for example, one major application ...

The proposed system utilises each of Non-Negative Matrix Factorization (NMF) and Latent Semantic Analysis (LSA) as an individual technique for feature selection. In addition to these two individual ...

Sayan Chakraborty, Arnab Bhattacharjee, Taps Maiti, Structural Factorization of Latent Adjacency Matrix, with an application to Auto Industry Networks, Sankhyā: The Indian Journal of Statistics, ...

To address the issue of insensitivity to high–resistance faults and the dependency of protection schemes on system topology in DC line protection, this paper proposes a single–ended DC line protection ...