Pregled bibliografske jedinice broj: 658719
Canonical Polyadic Decomposition For Unsupervised Linear Feature Extraction From Protein Profiles
Canonical Polyadic Decomposition For Unsupervised Linear Feature Extraction From Protein Profiles // Proceedings of the 21th European Signal Processing Conference (EUSIPCO 2013)
Marakeš, Maroko, 2013. (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 658719 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Canonical Polyadic Decomposition For Unsupervised Linear Feature Extraction From Protein Profiles
Autori
Jukić, Ante ; Kopriva, Ivica, Cichocki, Andrzej
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 21th European Signal Processing Conference (EUSIPCO 2013)
/ - , 2013
Skup
21th European Signal Processing Conference (EUSIPCO 2013)
Mjesto i datum
Marakeš, Maroko, 09.09.2013. - 13.09.2013
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
feature extraction; tensor decomposition; cancer prediction
Sažetak
We propose a method for unsupervised linear feature extraction through tensor decomposition. The linear feature extraction can be formulated as a canonical polyadic decomposition (CPD) of a third-order tensor when transformation matrix is constrained to be equal to the Khatri-Rao product of two matrices. Therefore, standard algorithms for computing CPD decomposition can be used for feature extraction. The proposed method is validated on publicly available low-resolution mass spectra of cancerous and non-cancerous samples. Obtained results indicate that this approach could be of practical importance in analysis of protein expression profiles.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
098-0982903-2558 - Analiza višespektralih podataka (Kopriva, Ivica, MZOS ) ( CroRIS)
Ustanove:
Institut "Ruđer Bošković", Zagreb
