D. Mandic

Publications
Influence

Multivariate empirical mode decomposition

N. Rehman, D. Mandic
Mathematics
Proceedings of the Royal Society A: Mathematical…
8 May 2010

Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, they… Expand

Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis

A. Cichocki, D. Mandic, +4 authors A. Phan
Mathematics, Computer Science
IEEE Signal Processing Magazine
17 March 2014

We show that higher-order tensors (i.e., multiway arrays) enable such a fundamental paradigm shift toward models that are essentially polynomial, the uniqueness of which, unlike the matrix methods, is guaranteed under very mild and natural conditions. Expand

Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability

D. Mandic, J. Chambers
Computer Science
7 August 2001

This book shows researchers how recurrent neural networks can be implemented to expand the range of traditional signal processing techniques. Expand

Multivariate multiscale entropy: a tool for complexity analysis of multichannel data.

M. U. Ahmed, D. Mandic
Computer Science, Medicine
Physical review. E, Statistical, nonlinear, and…
27 December 2011

This work generalizes the recently introduced univariate multiscale entropy (MSE) analysis to the multivariate case, to suit real-world biological and physical systems. Expand

Filter Bank Property of Multivariate Empirical Mode Decomposition

N. Rehman, D. Mandic
Computer Science
IEEE Transactions on Signal Processing
1 May 2011

The multivariate empirical mode decomposition (MEMD) algorithm has been recently proposed in order to make multivariate EMD suitable for processing of multichannel signals. Expand

A generalized normalized gradient descent algorithm

D. Mandic
Mathematics, Computer Science
IEEE Signal Processing Letters
30 January 2004

A generalized normalized gradient descent (GNGD) algorithm for linear finite-impulse response (FIR) adaptive filters. Expand

Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models

D. Mandic, V. Goh
Mathematics
26 May 2009

This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general… Expand

Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis

D. Mandic, N. Rehman, Zhaohua Wu, N. Huang
Computer Science
IEEE Signal Processing Magazine
16 October 2013

This article addresses data-driven time-frequency (T-F) analysis of multivariate signals, which is achieved through the empirical mode decomposition (EMD) algorithm and its noise assisted and multivariate extensions, the ensemble EEMD (EEMD), multivariate EMD (MEMD). Expand

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions

A. Cichocki, N. Lee, I. Oseledets, A. Phan, Q. Zhao, D. Mandic
Computer Science
Found. Trends Mach. Learn.
19 December 2016

We provide innovativesolutions to low-rank tensor network decompositions and easy to interpretgraphical representations of the mathematical operations ontensor networks. Expand

Multivariate Multiscale Entropy Analysis

M. U. Ahmed, D. Mandic
Mathematics, Computer Science
IEEE Signal Processing Letters
1 February 2012

We introduce multivariate sample entropy (MSampEn) and evaluate it over multiple time scales to perform the multivariate multiscale entropy (MMSE) analysis. Expand

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