PLANT DATA VISUALIZATION USING NON-NEGATIVE MATRIX FACTORIZATION

Plant Data Visualization Using Non-negative Matrix Factorization

Non-negative matrix factorization (NMF) is a method for dimensionality reduction and simplification of large data sets. Unlike tools such as principle components analysis (PCA) and factor analysis , NMF produces basis vectors that correspond to perceptible features in the original data. This is particularly useful when working with data where visual interpretation of the simplified representati...

Multimodal Image Collection Visualization Using Non-negative Matrix Factorization

In this paper we address the problem of generating an image collection visualization in which images and text can be projected together. Given a collection of images with attached text annotations, we aim to find a common representation for both information sources to model latent correlations among the collection. Using the proposed latent representation, an image collection visualization is b...

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...

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...