scispace - formally typeset
Open Access

Algorithms for non-negative matrix factorization

D Seung, +1 more
- Vol. 13, pp 556-562
About
The article was published on 2001-01-01 and is currently open access. It has received 5015 citations till now. The article focuses on the topics: Non-negative matrix factorization.

read more

Citations
More filters
Journal ArticleDOI

A Review of Automatic Drum Transcription

TL;DR: This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-specific challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems.
Journal ArticleDOI

Using non-negative matrix factorization in the “unmixing” of diffuse reflectance spectra

TL;DR: In this paper, a quantification technique is proposed and discussed which approaches the analysis of DRS data sets as a linear mixing problem and applies a non-negative matrix factorization (NMF) algorithm in their decomposition.
Proceedings Article

Constrained NMF-based multi-view clustering on unmapped data

TL;DR: This paper tackles the problem of multi-view clustering for unmapped data in the framework of NMF based clustering with the help of inter-view constraints, and gets good performance on unm mapped data, and outperforms existing algorithms on partially mapped data and completely mapped data.
Journal ArticleDOI

Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation

TL;DR: A new deep learning method, called semi-supervised graph regularized deep NMF with bi-orthogonal constraints (SGDNMF), which incorporates dual-hypergraph Laplacian regularization, which can reinforce high-order relationships in both data and feature spaces and fully retain the intrinsic geometric structure of the original data.
Journal ArticleDOI

Semantic smoothing for text clustering

TL;DR: The experimental results show that the clustering performance based on the S-VSM is better compared to the traditional VSM model and compares favorably against the standard GVSM kernel which uses word co-occurrences to compute the latent similarities between document terms.
Related Papers (5)