Convex Non-Negative Matrix Factorization for Brain Tumor Delimitation from MRSI Data

Convex Non-Negative Matrix Factorization for Brain Tumor Delimitation from MRSI Data

BACKGROUND Pattern Recognition techniques can provide invaluable insights in the field of neuro-oncology. This is because the clinical analysis of brain tumors requires the use of non-invasive methods that generate complex data in electronic format. Magnetic Resonance (MR), in the modalities of spectroscopy (MRS) and spectroscopic imaging (MRSI), has been widely applied to this purpose. The het...

Iterative Weighted Non-smooth Non-negative Matrix Factorization for Face Recognition

Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...

Non-negative matrix factorization

Convex Hull Convolutive Non-Negative Matrix Factorization for Uncovering Temporal Patterns in Multivariate Time-Series Data

We propose the Convex Hull Convolutive Non-negative Matrix Factorization (CH-CNMF) algorithm to learn temporal patterns in multivariate time-series data. The algorithm factors a data matrix into a basis tensor that contains temporal patterns and an activation matrix that indicates the time instants when the temporal patterns occurred in the data. Importantly, the temporal patterns correspond cl...

Automated Quality Control for Proton Magnetic Resonance Spectroscopy Data Using Convex Non-negative Matrix Factorization

Proton Magnetic Resonance Spectroscopy (H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a ...