ABSTRACT: In this paper, an Optimal Predictive Modeling of Nonlinear Transformations “OPMNT” method has been developed while using Orthogonal Nonnegative Matrix Factorization “ONMF” with the ...

Abstract: Matrix factorization is a central paradigm in matrix completion and collaborative filtering. Low-rank factorizations have been extremely successful in reconstructing and generalizing ...

Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the ...

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...

College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China ...

STNMF with Accelerated Fast HALS (AF-HALS) converges faster than the previous implementation of STNMF. Reconstruction error of STNMF implemented with AF-HALS (black ...

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States ...

Abstract: In this paper, we propose a new low-rank matrix factorization model, dubbed bounded simplex-structured matrix factorization (BSSMF). Given an input matrix X and a factorization rank r, BSSMF ...