Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Zuyuan Yang"'
Publikováno v:
Sensors, Vol 23, Iss 8, p 3891 (2023)
Spot detection has attracted continuous attention for laser sensors with applications in communication, measurement, etc. The existing methods often directly perform binarization processing on the original spot image. They suffer from the interferenc
Externí odkaz:
https://doaj.org/article/805c319380884ad085f8267e01dd5e1a
Publikováno v:
IEEE Access, Vol 7, Pp 176541-176551 (2019)
It is crucial to robustly estimate the number of speakers (NoS) from the recorded audio mixtures in a reverberant environment. Some popular time-frequency (TF) methods approach this NoS estimation problem by assuming that only one of the speech compo
Externí odkaz:
https://doaj.org/article/1b26974677f04a5090528a7358577318
Publikováno v:
IEEE Access, Vol 7, Pp 166380-166389 (2019)
As a typical variation of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) is capable of exploiting information of the cluster embedded in the matrix of similarity. The traditional SNMF-based methods for clustering first performs the tech
Externí odkaz:
https://doaj.org/article/35afbedbbc0b417ca0987cd4d7b2b725
Publikováno v:
IEEE Access, Vol 6, Pp 65239-65249 (2018)
Transform learning has been proposed as a new and effective formulation for analysis dictionary learning, where the ℓ0 norm or the ℓ1 norm are generally used as sparsity constraint. The sparse solutions can be obtained by the hard thresholding or
Externí odkaz:
https://doaj.org/article/150c7a9be17446989175effe497256aa
Publikováno v:
IEEE Access, Vol 6, Pp 77953-77964 (2018)
Dimension reduction (DR) is an essential preprocessing for hyperspectral image (HSI) classification. Recently, nonnegative matrix factorization (NMF) has been shown as an effective tool for the DR of hyperspectral data given the fact that it provides
Externí odkaz:
https://doaj.org/article/2a42fb6bde64417b85ef87c22385e555
Publikováno v:
IEEE Access, Vol 4, Pp 5161-5168 (2016)
Nonnegative matrix factorization (NMF) is a hot topic in machine learning and data processing. Recently, a constrained version, non-smooth NMF (NsNMF), shows a great potential in learning meaningful sparse representation of the observed data. However
Externí odkaz:
https://doaj.org/article/470837ebd9d645fba461624870b9bd13
Sparse Gene Coexpression Network Analysis Reveals EIF3J-AS1 as a Prognostic Marker for Breast Cancer
Publikováno v:
Complexity, Vol 2018 (2018)
Predictive and prognostic biomarkers facilitate the selection of treatment strategies that can improve the survival of patients. Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in cancer progression, with diagn
Externí odkaz:
https://doaj.org/article/f1a89bea12f943fe95513e38a4676a28
Publikováno v:
IEEE Access, Vol 3, Pp 358-367 (2015)
In a machine-to-machine network, the throughput performance plays a very important role. Recently, an attractive energy harvesting technology has shown great potential to the improvement of the network throughput, as it can provide consistent energy
Externí odkaz:
https://doaj.org/article/404129e75a58445f9c9762b75409a9c4
Publikováno v:
Neurocomputing. 512:443-455
Publikováno v:
IEEE Transactions on Cybernetics. 52:10785-10799
Convolutional transform learning (CTL), learning filters by minimizing the data fidelity loss function in an unsupervised way, is becoming very pervasive, resulting from keeping the best of both worlds: the benefit of unsupervised learning and the su