libri scuola books Fumetti ebook dvd top ten sconti 0 Carrello


Torna Indietro
ARGOMENTO:  BOOKS > INFORMATICA > TESTI GENERALI

christian jutten; dana lahat; tulay adali - matrix and tensor decomposition
Zoom

Matrix and Tensor Decomposition Application to Data Fusion and Analysis

; ;




Disponibilità: Non disponibile o esaurito presso l'editore


PREZZO
149,98 €



Questo prodotto usufruisce delle SPEDIZIONI GRATIS
selezionando l'opzione Corriere Veloce in fase di ordine.


Pagabile anche con Carta della cultura giovani e del merito, 18App Bonus Cultura e Carta del Docente


Facebook Twitter Aggiungi commento


Spese Gratis

Dettagli

Genere:Libro
Lingua: Inglese
Pubblicazione: 02/2024





Note Editore

Matrix and Tensor Decomposition: Application to Data Fusion and Analysis introduces the main theoretical concepts for data fusion using matrix and tensor decompositions, beginning with the concept of "diversity", which facilitates identifiability. It provides the link between theoretical results and practice by addressing key implementation issues, such as model choice for a given problem, identification of sources of diversity, parameter selection and performance evaluation. Using rich diagrams to help communicate the main ideas and relationships among models and methods, this book presents a readily accessible reference for researchers on the methods and application of matrix and tensor decompositions.





Sommario

1. Introduction 2. ICA and IVA: A Bottom-up Approach 3. ICA and IVA: A Top-down Approach 4. Sparse Decompositions 5. Nonnegative Decompositions 6. Tensor Decompositions 7. Data Fusion and Analysis Through 8. Data Fusion and Analysis Using General 9. Implementation Issues and Open





Autore

Christian Jutten received a PhD degree in 1981 and the Docteur ès Sciences degree in 1987 from the Institut National Polytechnique of Grenoble (France). He is currently deputy director of Institute for Information Sciences and Technologies of CNRS. He has been deputy director of the Grenoble images, speech, signal and control laboratory (GIPSA) and director of the Department Images-Signal (DIS from 2007 to 2010. For 30 years, his research interests have been blind source separation, independent component analysis and learning in neural networks, including theoretical aspects (separability, source separation in nonlinear mixtures, sparsity) and applications in signal processing (biomedical, seismic, hyperspectral imaging, speech). He is author or co-author of more than 75 papers in international journals, four books, 25 invited plenary talks and 170 communications in international conferences. He received the Medal Blondel in 1997 from SEE (French Electrical Engineering society) for his contributions in source separation and independent component analysis, and has been elevated as a Fellow IEEE and a senior Member of Institut Universitaire de France in 2008. In 2012, he was awarded by an ERC Advanced Grant CHESS. In 2013, he has been elevated as EURASIP Fellow and reconducted for five years as a senior member of Institut Universitaire de France.
Dana Lahat received the BSc, MSc and PhD degrees in electrical and electronics engineering from Tel Aviv University, Israel, in 1998, 2004 and 2013, respectively. She is currently a postdoctoral researcher in GIPSA-Lab, Grenoble, France. She has been awarded the Chateaubriand Fellowship of the French Government for the academic year 2007-2008. Her main research interests are statistical signal processing and source separation
Tülay Adali received the Ph.D. degree in Electrical Engineering from North Carolina State University, Raleigh, NC, USA, in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, MD, the same year. She is currently a Distinguished University Professor in the Department of Computer Science and Electrical Engineering at UMBC and is the director of the Machine Learning for Signal Processing Lab (MLSP Lab).










Altre Informazioni

ISBN:

9780128157602

Condizione: Nuovo
Formato: Brossura
Pagine Arabe: 400


Dicono di noi