Analysis of Multi-Tiered Social Networks in Rural Area 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...

Multi-Task Clustering using Constrained Symmetric Non-Negative Matrix Factorization

Researchers have attempted to improve the quality of clustering solutions through various mechanisms. A promising new approach to improve clustering quality is to combine data from multiple related datasets (tasks) and apply multi-task clustering. In this paper, we present a novel framework that can simultaneously cluster multiple tasks through balanced Intra-Task (within-task) and Inter-Task (...

Non-negative matrix factorization

Non-negative Matrix Factorization via Archetypal Analysis

Given a collection of data points, non-negative matrix factorization (NMF) suggests to express them as convex combinations of a small set of ‘archetypes’ with non-negative entries. This decomposition is unique only if the true archetypes are non-negative and sufficiently sparse (or the weights are sufficiently sparse), a regime that is captured by the separability condition and its generalizati...

Email Surveillance Using Non-negative Matrix Factorization

In this study, we apply a non-negative matrix factorization approach for the extraction and detection of concepts or topics from electronic mail messages. For the publicly released Enron electronic mail collection, we encode sparse term-by-message matrices and use a low rank non-negative matrix factorization algorithm to preserve natural data non-negativity and avoid subtractive basis vector an...