Position-dependent motif characterization using non-negative matrix factorization

Position-dependent motif characterization using non-negative matrix factorization

MOTIVATION Cis-acting regulatory elements are frequently constrained by both sequence content and positioning relative to a functional site, such as a splice or polyadenylation site. We describe an approach to regulatory motif analysis based on non-negative matrix factorization (NMF). Whereas existing pattern recognition algorithms commonly focus primarily on sequence content, our method simult...

Non-negative matrix factorization

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...

Position-Aware Non-negative Matrix Factorization for Satellite Image Representation

Satellite images clustering is a challenging problem in remote sensing and machine vision, where each image content is represented by a high-dimensional feature vector. However, the feature vectors might not be appropriate to express the semantic content of images, which eventually leads to poor results in clustering and classification. To tackle this problem, we propose a novel approach to gen...

A new approach for building recommender system using non negative matrix factorization method

Nonnegative Matrix Factorization is a new approach to reduce data dimensions. In this method, by applying the nonnegativity of the matrix data, the matrix is ​​decomposed into components that are more interrelated and divide the data into sections where the data in these sections have a specific relationship. In this paper, we use the nonnegative matrix factorization to decompose the user ratin...