Results 41 to 50 of about 244,754 (100)

Audio Spotforming Using Nonnegative Tensor Factorization with Attractor-Based Regularization [PDF]

Spotforming is a target-speaker extraction technique that uses multiple microphone arrays. This method applies beamforming (BF) to each microphone array, and the common components among the BF outputs are estimated as the target source.
Ayano, Shoma, Li, Li, Kitamura, Daichi, Seki, Shogo   +3 more
core  

Coseparable Nonnegative Tensor Factorization With T-CUR Decomposition [PDF]

Nonnegative Matrix Factorization (NMF) is an important unsupervised learning method to extract meaningful features from data. To address the NMF problem within a polynomial time framework, researchers have introduced a separability assumption, which has ...
Wei, Yimin, Chen, Juefei, Huang, Longxiu
core  

NONNEGATIVE TENSOR FACTORIZATION FOR SOURCE SEPARATION OF LOOPS IN AUDIO

, 2018
The prevalence of exact repetition in loop-based music makes it an opportune target for source separation. Nonnegative factorization approaches have been used to model the repetition of looped content, and kernel additive modeling has leveraged ...
Smith, JBL, Goto, M, IEEE
core   +1 more source

Uncovering novel mutational signatures by <i>de novo</i> extraction with SigProfilerExtractor. [PDF]

Cell Genom, 2022
Islam SMA, Díaz-Gay M, Wu Y, Barnes M, Vangara R, Bergstrom EN, He Y, Vella M, Wang J, Teague JW, Clapham P, Moody S, Senkin S, Li YR, Riva L, Zhang T, Gruber AJ, Steele CD, Otlu B, Khandekar A, Abbasi A, Humphreys L, Syulyukina N, Brady SW, Alexandrov BS, Pillay N, Zhang J, Adams DJ, Martincorena I, Wedge DC, Landi MT, Brennan P, Stratton MR, Rozen SG, Alexandrov LB.   +34 more
europepmc   +1 more source

An all-at-once approach to nonnegative tensor factorizations

, 2008
Tensors can be viewed as multilinear arrays or generalizations of the notion of matrices. Tensor decompositions have applications in various fields such as psychometrics, signal processing, numerical linear algebra and data mining.
Flores Garrido, Marisol
core  

Nesterov-based alternating optimization for nonnegative tensor factorization: algorithm and parallel implementation

, 2018
Summarization: We consider the problem of nonnegative tensor factorization. Our aim is to derive an efficient algorithm that is also suitable for parallel implementation.
Kostoulas Georgios(http://users.isc.tuc.gr/~gkostoulas), Liavas Athanasios(http://users.isc.tuc.gr/~aliavas), Lourakis Georgios(http://users.isc.tuc.gr/~glourakis), Σιδηροπουλος Νικολαος(http://users.isc.tuc.gr/~nsidiropoulos), Sidiropoulos Nikolaos(http://users.isc.tuc.gr/~nsidiropoulos), Huang Kejun, Λιαβας Αθανασιος(http://users.isc.tuc.gr/~aliavas), Λουρακης Γεωργιος(http://users.isc.tuc.gr/~glourakis), Κωστουλας Γεωργιος(http://users.isc.tuc.gr/~gkostoulas)   +8 more
core   +1 more source

Secured Secret Sharing of QR Codes Based on Nonnegative Matrix Factorization and Regularized Super Resolution Convolutional Neural Network. [PDF]

Sensors (Basel), 2022
Velumani R, Sudalaimuthu H, Choudhary G, Bama S, Jose MV, Dragoni N.   +5 more
europepmc   +1 more source

Variational bayesian partially observed non-negative tensor factorization

, 2018
Non-negative matrix and tensor factorization (NMF/NTF) have become important tools for extracting part based representations in data. It is however unclear when an NMF or NTF approach is most suited for data and how reliably the models predict when ...
Soren F. V. Nielsen, Hinrich, Jesper Løve, Jesper L. Hinrich, Madsen, Kristoffer Hougaard, Morten Morup, Nielsen, Søren Føns Vind, Mørup, Morten, Kristoffer H. Madsen   +7 more
core   +1 more source

On the geometric interpretation of the nonnegative rank [PDF]

The nonnegative rank of a nonnegative matrix is the minimum number of nonnegative rank-one factors needed to reconstruct it exactly. The problem of determining this rank and computing the corresponding nonnegative factors is difficult; however it has ...
GILLIS, Nicolas, GLINEUR, François
core  

Enhanced tensor factorization framework using non-negative and probabilistic tensor factorization approaches for microblogging content propagation modelling

, 2017
With the aim of identifying the user’s preferences, Content propagation modeling from the micro-blogging sites aids diverse organizations. In existing studies, four user behavior aspects were used by the content propagation model that is to say topic ...
A. Kavitha, A. Marimuthu, N. Baggyalakshmi   +2 more
core   +1 more source