Deep Non-Negative Matrix Factorization (DNMF) methods provide an efficient low-dimensional representation of given data through their layered architecture. A limitation of such methods is that they cannot effectively preserve the local and global geometric structures of the data in each layer. ...
Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability. However, the existing NMF-based methods have the following three problems: 1) they directly transform the original network into community membership space, so it is...
Deep nonnegative matrix factorization (deep NMF) has recently emerged as a valuable technique for extracting multiple layers of features across different scales. However, all existing deep NMF models and algorithms have primarily centered their evaluation on the least squares error, which may not be ...
CDNMF: Contrastive Deep Nonnegative Matrix Factorization for Community Detection The implementation of our paper "Contrastive Deep Nonnegative Matrix Factorization for Community Detection". (ICASSP 2024, CCF B) Overview We introduce the idea of contrastive learning (CL) into the nonnegative matrix fact...
Paper tables with annotated results for Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Abstract Multiplex networks convey more valuable information than single-layer networks; thus, performing the community detection task involving these networks has become a subject of extensive research on the exploration of latent community structures. The non-negative matrix factorization (NMF) algorithm ...
2. DEEP NON-NEGATIVE MATRIX FACTORIZATION NMF operates on a matrix of F -dimensional non-negative spectral features, usually the power or magnitude spectrogram of the mixture, M = [m1 ··· mT ], where T is the number of frames and mt ∈ RF+, t = 1, . . . , T are obtained ...
Non-negative matrix factorization(NMF) is widely used in solving the issue of link prediction due to its good interpretability and scalability. However,most existing NMF-based approaches involve shallow decoder models, which are incapable of capturing complex hierarchical information hidden in networks, ...
2.2Nonnegative matrix factorization Nonnegative matrix factorization methods: NMF is a low-rank matrix factorization model that has been around for a long time [24]. Given a nonnegative matrix\(X=[X_{1},X_{2},..,X_{n}]\), which is formed from a collection ofnm-dimension data vectors...
To overcome this shortcoming, a nonnegative matrix factorization (NMF) model is introduced for matrix factorization [8]. The core idea of NMF is to decompose a nonnegative matrix X into the inner product of a nonnegative basis matrix W and a nonnegative coefficient matrix H, such that X≈...