Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Laming Chen"'
Publikováno v:
IEEE Signal Processing Letters. 23:934-938
Square-root least absolute shrinkage and selection operator (Lasso), a variant of Lasso, has recently been proposed with a key advantage that the optimal regularization parameter is independent of the noise level in the measurements. In this letter,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5fdf311f52d3a82641f23d40639b7d24
https://doi.org/10.1109/lsp.2016.2567482
https://doi.org/10.1109/lsp.2016.2567482
Publikováno v:
RecSys
Online Video Streaming services such as Hulu hosts tens of millions of premium videos, which requires an effective recommendation system to help viewers discover what they enjoy. In this talk, we will introduce Hulu's recent technical progresses in r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b22b3b52014e09b0ace1faa3323eb22f
https://doi.org/10.1145/3240323.3241730
https://doi.org/10.1145/3240323.3241730
Autor:
Laming Chen, Yuantao Gu
Publikováno v:
IEEE Signal Processing Letters. 22:1600-1603
The literature on sparse recovery often adopts the ${\ell _p}$ “norm” ( $p \in [0,1]$ ) as the penalty to induce sparsity of the signal satisfying an underdetermined linear system. The performance of the corresponding ${\ell _p}$ minimization pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9566fe6567a479b3ee599a5b7633cc9b
https://doi.org/10.1109/lsp.2015.2416003
https://doi.org/10.1109/lsp.2015.2416003
Publikováno v:
DSP
This article discusses the performance of the oracle receiver in recovering high dimensional sparse signals, which possesses the knowledge of the signals' support set. We consider a general framework, in which the sensing matrix and the measurements
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c1da18e32423765e0f0d1e2385e6049
https://doi.org/10.1109/icdsp.2016.7868648
https://doi.org/10.1109/icdsp.2016.7868648
Autor:
Yuantao Gu, Laming Chen
Publikováno v:
GlobalSIP
Recovering sparse signals from noisy underdetermined linear measurements has been a concerning problem in the signal processing community. Lasso has been put forward to handle this problem well, yet recent research reveals that replacing l1 norm with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc58213a223aed3f4bebb1f7cc6dce2a
https://doi.org/10.1109/globalsip.2015.7418403
https://doi.org/10.1109/globalsip.2015.7418403
Publikováno v:
ICASSP
Sparse signal recovery in the static case has been well studied under the framework of Compressive Sensing (CS), while in recent years more attention has also been paid to the dynamic case. In this paper, enlightened by the idea of modified-CS with p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09cbc3c9a22f35f4feb023f3a01c2770
https://doi.org/10.1109/icassp.2015.7179020
https://doi.org/10.1109/icassp.2015.7179020
Autor:
Laming Chen, Yuantao Gu
Publikováno v:
ICASSP
In solving the problem of sparse recovery, non-convex techniques have been paid much more attention than ever before, among which the most widely used one is l p minimization with p ∈ (0, 1). It has been shown that the global optimality of l p mini
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3debc604c115fafe4c0f2d8f5a1cabfb
https://doi.org/10.1109/icassp.2015.7178641
https://doi.org/10.1109/icassp.2015.7178641
Autor:
Yuantao Gu, Laming Chen
Publikováno v:
DSP
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3385b70fb26c73b98ec284c22743e71
https://doi.org/10.1109/icdsp.2014.6900686
https://doi.org/10.1109/icdsp.2014.6900686
Publikováno v:
ICASSP
Compressive sensing (CS) is a data acquisition technique that measures sparse or compressible signals at a sampling rate lower than their Nyquist rate. Results show that sparse signals can be reconstructed using greedy algorithms, often requiring pri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7943df8fd0cac2e47a56e83f8cee1a55
https://doi.org/10.1109/icassp.2014.6854225
https://doi.org/10.1109/icassp.2014.6854225