Semantic Tagging-Based Document Retrieval Using Non-Negative Matrix Factorization

Clinical Document Clustering using Multi-view Non-Negative Matrix Factorization

Clinical document contains vital information like symptom names, medication names, age, gender and some demographical information. These information can be used for giving quick relief from a disease. In existing system, they had built a system for clustering symptom names and medication names using Multi-View Non-Negative Matrix Factorization. While considering the clinical documents the facto...

Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization

Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the l2 distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of ...

Automatic generic document summarization based on non-negative matrix factorization

Article history: Received 20 August 2007 Received in revised form 11 February 2008 Accepted 13 June 2008 Available online 8 August 2008

Parallel Non Negative Matrix Factorization for Document Clustering

Non-negative matrix factorization has been used as an effective approach for document clustering lately. One advantage of this method is that clustering results can be directly concluded from the factor matrices. This project gives parallel implementation of three algorithms for Non-negative matrix factorization. Experiments of these parallel algorithms for large datasets shows good speedup for...

Non-negative matrix factorization