Matrix and tensor decomposition have been widely used in various domains, from signal processing, machine learning to neuroscience, as powerful tools for dimensionality reduction, feature extraction, clustering, and classification. The main reason behind their applications is the natural high/multi-dimensional array structure of modern datasets that provides good opportunities to exploit matrix and tensor decomposition-based approaches. More specifically, matrix/tensor decomposition allows factorizing data into several basis components, and hence, can reveal important hidden patterns and new insights from the data. However, recent years have witnessed a rapid increase in "Big Data" of which speed, variety, and volume of information are significantly growing over time. It has led to many issues that are still challenging for the state-of-the-art matrix and tensor decomposition methods, such as (i) large-scale matrix/tensor decomposition, (ii) fast adaptive decomposition of multivariate and high-dimensional data streams, (iii) decomposition in the presence of data corruption, uncertainty, and imperfection, to name a few. Therefore, it is imperative to develop new algorithms and techniques capable of dealing with such issues as well as to demonstrate their use with new applications. This special session is about recent advances in the important field of matrix and tensor decomposition which includes:

• New matrix decomposition algorithms
• Subspace estimation and tracking
• Fast algorithms for tensor decomposition
• Streaming tensor decomposition
• Robust tensor/matrix decomposition
• Robust subspace tracking
• New applications of tensor/matrix decomposition

Dr. Le Trung Thanh, University of Orléans, France Biography: Le Trung Thanh received the B.Sc. and M.Sc. degrees in Electronics and Communications from the VNU University of Engineering and Technology, Hanoi, Vietnam in 2016 and 2018 respectively, and the Ph.D. degree in Signal Processing from the University of Orleans, INSA CVL, PRISME, France in 2022. He is currently a postdoctoral researcher at the University of Orleans, France. His research interests include signal processing, subspace tracking, tensor analysis, and system identification.

Prof. Karim Abed-Meraim, University of Orléans, France Biography: Karim Abed-Meraim (Fellow, IEEE) was born in 1967. He received the State Engineering Degree from the École Polytechnique, Palaiseau, France, in 1990, the State Engineering Degree from the École Nationale Supérieure des Télécommunications (ENST), Paris, France, in 1992, the M.Sc. degree from Paris XI University, Orsay, France, in 1992, and the Ph.D. degree in the field of signal processing and communications from ENST, in 1995. From 1995 to 1998, he was a Research Staff with the Electrical Engineering Department, The University of Melbourne, where he worked on several research projects related to "Blind System Identification for Wireless Communications", "Blind Source Separation," and "Array Processing for Communications". From 1998 to 2012, he has been an Assistant Professor, then an Associate Professor with the Signal and Image Processing Department, Télécom ParisTech. In September 2012, he joined the University of Orléans, France (PRISME Laboratory), as a Full Professor. His research interests include signal processing for communications, adaptive filtering and tracking, array processing, and statistical performance analysis. He is the author of about 500 scientific publications, including book chapters, international journal and conference papers, and patents. Dr. Abed-Meraim is currently a member of the IEEE SAM-TC and a Senior Area Editor of the IEEE Transactions on Signal Processing.