Tensor decomposition has been demonstrated to be successful in a wide range of
applications, from neuroscience and wireless communications to social networks. In an …

The past century has witnessed the grand success of brain imaging technologies, such as
electroencephalography and magnetic resonance imaging, in probing cognitive states and …

Canonical Polyadic (CP) decomposition is a powerful multilinear algebra tool for analyzing
multiway (aka tensor) data and has been used for various signal processing and machine …

With the prevalence of commercial WiFi devices and the development of Internet of Things
(IoT), researchers have extended the usage of WiFi from communication to sensing …

In this paper, we introduce a fast adaptive algorithm for CAN-DECOMP/PARAFAC
decomposition of streaming three-way tensors using randomized sketching techniques. By …

A tensor-input/tensor-output Recursive Exponentially Weighted N-Way Partial Least
Squares (REW-NPLS) regression algorithm is proposed for high dimension multi-way …

Multiple-input multiple-output (MIMO) systems employing multicarrier modulation (MCM),
including flexible MCM that unifies several MCM schemes, have been well studied recently …

The capability of constructing structural features of EEG stream has long been pursued to
track the events and abnormalities correlating multiple data domains in a variant time scale …

The so-called block-term decomposition (BTD) tensor model, especially in its rank-(L r, L r,
1) version, has been recently receiving increasing attention due to its enhanced ability of …

Vibration signal analysis is one of the most effective approaches for detecting faults in
bearings. A bearing compound-fault signal always consists of multiple signatures and …