Evaluation of non-negative matrix factorization of grey matter in age prediction

Evaluation of non-negative matrix factorization of grey matter in age prediction.

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we ev...

Non-negative matrix factorization

Link prediction based on non-negative matrix factorization

With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like information science, anthropology, sociology and computer sciences. It makes requirements for effective link prediction techniques to extract the most e...

Software Defect Prediction Using Non-Negative Matrix Factorization

Quality is considered as an important issue in the fields of software engineering. However, building quality software is very expensive, in order to raise the effectiveness and efficiency of quality assurance and testing, software defect prediction is used to identify defect-prone modules in an upcoming version of a software system and help to allow the effort on those modules. Although many mo...

Bayesian Non-negative Matrix Factorization

We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates. We discuss how the Gibbs sampler can be used for model ...