Results 231 to 240 of about 114,074 (272)

Sparse Bayesian Learning for Robust PCA

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
In this paper, we propose a new Bayesian model to solve the Robust PCA problem - recovering the underlying low-rank matrix and sparse matrix from their noisy compositions. We first derive and analyze a new objective function, which is proven to be equivalent to the fundamental minimizing "rank+sparsity" objective.
Jing Liu 0009, Yacong Ding, Bhaskar Rao 0001   +2 more
openaire   +1 more source

Preference Learning to Rank with Sparse Bayesian

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 2009
In this paper, we propose a sparse Bayesian approach to learn ranking function from labeled data. The ranking function can be used to define an ordering among documents according to their degree of relevance to the user query. This ranking function is more efficient and accurate than the function leaned by proposed approaches.
Xiao Chang, Qinghua Zheng
openaire   +1 more source

Parameter identifiability in Sparse Bayesian Learning

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
The problem of parameter identifiability in linear underdetermined models is addressed, where the observed data vectors follow a multivariate Gaussian distribution. The problem is underdetermined because the dimension of parameters characterizing the distribution of the data is larger than the dimension of the observed vectors.
Pal, Piya, Vaidyanathan, P. P.
openaire   +2 more sources

Bayesian learning for sparse signal reconstruction

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004
Sparse Bayesian learning and specifically relevance vector machines have received much attention as a means of achieving parsimonious representations of signals in the context of regression and classification. We provide a simplified derivation of this paradigm from a Bayesian evidence perspective and apply it to the problem of basis selection from ...
David P. Wipf, Bhaskar D. Rao
openaire   +1 more source

Sparse Bayesian Learning for Basis Selection

IEEE Transactions on Signal Processing, 2004
Sparse Bayesian learning (SBL) and specifically relevance vector machines have received much attention in the machine learning literature as a means of achieving parsimonious representations in the context of regression and classification. The methodology relies on a parameterized prior that encourages models with few nonzero weights. In this paper, we
David P. Wipf, Bhaskar D. Rao
openaire   +1 more source

2D Beamforming on Sparse Arrays with Sparse Bayesian Learning

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
Sparse arrays such as co-prime and nested arrays can identify more sources than the number of sensors. This is because their difference co-arrays contain a uniformly spaced virtual array with more elements than the number of sensors in the array. In this paper we demonstrate this using two dimensional co-prime and nested sparse arrays combined with ...
Santosh Nannuru, Peter Gerstoft
openaire   +1 more source

Bootstrapped sparse Bayesian learning for sparse signal recovery

2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
In this article we study the sparse signal recovery problem in a Bayesian framework using a novel Bootstrapped Sparse Bayesian Learning method. Sparse Bayesian Learning (SBL) framework is an effective tool for pruning out the irrelevant features and ending up with a sparse representation.
Ritwik Giri, Bhaskar D. Rao
openaire   +1 more source

Scalable Mean-Field Sparse Bayesian Learning

IEEE Transactions on Signal Processing, 2019
Sparse Bayesian learning is a powerful framework for expressing compressed sensing problems in the language of Bayesian inference, but its principal algorithm—the variational relevance vector machine—is matrix-bound and scales poorly to larger problem sizes.
Bradley Worley
exaly   +2 more sources

Sparse Bayesian Learning for Acoustic Source Localization

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
The localization of acoustic sources is a parameter estimation problem where the parameters of interest are the direction of arrivals (DOAs). The DOA estimation problem can be formulated as a sparse parameter estimation problem and solved using compressive sensing (CS) methods.
Ruchi Pandey, Santosh Nannuru, Aditya Siripuram   +2 more
openaire   +1 more source

Bayesian Learning of Sparse Multiscale Image Representations

IEEE Transactions on Image Processing, 2013
Multiscale representations of images have become a standard tool in image analysis. Such representations offer a number of advantages over fixed-scale methods, including the potential for improved performance in denoising, compression, and the ability to represent distinct but complementary information that exists at various scales.
James Michael Hughes, Daniel N. Rockmore, Yang Wang 0020   +2 more
openaire   +3 more sources