Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

Tensor modeling and algorithms for computing various tensor decompositions (the
Tucker/HOSVD and CP decompositions, as discussed here, most notably) constitute a very …

In this paper, multilead electrocardiogram (MECG) data compression using singular value
decomposition in multiresolution domain is proposed. It ensures a high compression ratio by …

In contemporary machine learning multidimensional rather than pure vector like data are
frequently encountered. Traditionally, such multidimensional objects, such as color images …

This paper presents a method for content change detection in multidimensional video
signals. Video frames are represented as tensors of order consistent with signal dimensions …

In hyperspectral remote sensing imagery, the sensor, atmosphere, topography, and other
factors often bring about some degradations, such as noise, haze, clouding, and shadowing …

We present a novel nonnegative tensor decomposition method, called Legendre
decomposition, which factorizes an input tensor into a multiplicative combination of …

This paper uses principal component analysis (PCA) to determine the spatial pattern of total
electron content (TEC) anomalies in the ionosphere post the China's Wenchuan Earthquake …

The electrocardiogram (ECG) is a biomedical signal that is widely used to monitor the heart
and diagnose cardiac problems. Depending on the clinical need, the ECG is recorded with …

This paper develops a novel decentralized dimensionality reduction algorithm for the
distributed tensor data across sensor networks. The main contributions of this paper are as …