The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

Purpose: This study sought to review the characteristics, strengths, weaknesses variants,
applications areas and data types applied on the various Dimension Reduction techniques …

Electromyographic (EMG) signals sensed at the skin surface on the forearm can be used to
accurately infer wrist-hand poses. However this is only possible when the EMG sensors are …

Multiview canonical correlation analysis (MCCA) seeks latent low-dimensional
representations encountered with multiview data of shared entities (aka common sources) …

Canonical correlation analysis (CCA) is a powerful technique for discovering whether or not
hidden sources are commonly present in two (or more) datasets. Its well-appreciated merits …

We present a powerful solution to the problem of vector set classification, based on a novel
goodness-of-fit metric, referred to as signal subspace matching (SSM). Unlike the existing …

Signal-based automatic condition monitoring techniques are one of the effective ways to
ensure the operational safety of modern industrial systems. Currently, the main challenge is …

We propose a generalized CUR (GCUR) decomposition for matrix pairs (A,B). Given
matrices A and B with the same number of columns, such a decomposition provides low …

Principal component analysis (PCA) is one of the most fundamental techniques for Big Data
analytics in eg, smart manufacturing and biostatistics, which is capable of extracting the most …

This article investigates the potential of surface electromyography (sEMG) as a new
biometric modality and proposes a deep neural network architecture as the backbone of a …