The program runs each method 100 times to measure average execution time and compares the performance against expected computational complexity. Cramer's Rule computes each variable xᵢ as the ratio ...

One of the fundamental operations in machine learning is computing the inverse of a square matrix. But not all matrices have an inverse. The most common way to check if a matrix has an inverse or not ...

QFD (quality function deployment) is a family of tools that can help engineers during the early stages of product development. A method of assessing options for new product definition, QFD can help ...

Magnetic resonance imaging (MRI) is among the most commonly used imaging methods in preclinical studies as it non-invasively produces multiparametric data of tissues and organs. An animal organism’s ...

Abstract: The low-rank property of seismic data has been successfully used for attenuating seismic random noise using a rank-reduction processing; however, traditional rank-reduction methods based on ...

Abstract: We propose a new dimension reduction method for matrix-valued data called Matrix Non-linear PCA (MNPCA), which is a non-linear generalization of (2D) ${}^{2}$ PCA. MNPCA is based on ...

Computer scientists are a demanding bunch. For them, it’s not enough to get the right answer to a problem — the goal, almost always, is to get the answer as efficiently as possible. Take the act of ...

Spatial Multi-Omics PCA (SMOPCA) is a novel dimension reduction method to integrate multi-modal data and extract low-dimensional representations with preserved spatial dependencies among spots. SMOPCA ...