Graph Regularized NMF with L20-norm for Unsupervised Feature Learning

arXiv:2403.10910v1 Announce Type: new
Abstract: Nonnegative Matrix Factorization (NMF) is a widely applied technique in the fields of machine learning and data mining. Graph Regularized Non-negative Matrix Factorization (GNMF) is an extension of NMF that incorporates graph regularization constraints. GNMF has demonstrated exceptional performance in clustering and dimensionality reduction, effectively discovering inherent low-dimensional structures embedded within high-dimensional spaces. However, the sensitivity of GNMF to noise limits its stability and robustness in practical applications. In order to enhance feature sparsity and …

abstract arxiv clustering constraints cs.lg data data mining dimensionality extension factorization feature fields graph machine machine learning matrix mining negative norm performance regularization type unsupervised