Results 241 to 250 of about 114,074 (272)

Sparse Bayesian learning for efficient visual tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
This paper extends the use of statistical learning algorithms for object localization. It has been shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM. While this SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data ...
Oliver Williams, Andrew Blake 0001, Roberto Cipolla   +2 more
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Sparse Bayesian learning for beamforming using sparse linear arrays

The Journal of the Acoustical Society of America, 2018
Sparse linear arrays such as co-prime and nested arrays can resolve more sources than the number of sensors. In contrast, uniform linear arrays (ULA) cannot resolve more sources than the number of sensors. This paper demonstrates this using Sparse Bayesian learning (SBL) and co-array MUSIC for single frequency beamforming.
Santosh, Nannuru, Ali, Koochakzadeh, Kay L, Gemba, Piya, Pal, Peter, Gerstoft   +4 more
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Sparse Bayesian learning with uncertain sensing matrix

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
Sparse Bayesian learning is a sparse processing method used for solving high-dimensional, underdetermined linear equations. Often the sensing matrix in the system of equations is assumed known and in presence of perturbations in this matrix performance of sparse processing degrades.
Santosh Nannuru, Peter Gerstoft, Kay L. Gemba   +2 more
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Dynamical System Implementations of Sparse Bayesian Learning

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019
Despite its state of the art performance in many applications, the sparse Bayesian learning (SBL) procedure can be expensive to implement, limiting its use in practice. In this paper, we use the locally competitive algorithm (LCA) framework to develop two continuous time dynamical systems whose trajectories converge to a minimum of the SBL objective ...
Matthew R. O'Shaughnessy, Mark A. Davenport, Christopher J. Rozell   +2 more
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Active learning for sparse bayesian multilabel classification

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014
We study the problem of active learning for multilabel classification. We focus on the real-world scenario where the average number of positive (relevant) labels per data point is small leading to positive label sparsity. Carrying out mutual information based near-optimal active learning in this setting is a challenging task since the computational ...
Deepak Vasisht, Andreas C. Damianou, Manik Varma, Ashish Kapoor   +3 more
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Bayesian learning of sparse gene regulatory networks

Biosystems, 2007
Differential equations (DEs) have been the most widespread formalism for gene regulatory network (GRN) modeling, as they offer natural interpretation of biological processes, easy elucidation of gene relationships, and the capability of using efficient parameter estimation methods. However, an important limitation of DEs is their requirement of O(d(2))
Zeke S. H. Chan, Lesley Collins, Nikola K. Kasabov   +2 more
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Decentralized Bayesian learning of jointly sparse signals

2014 IEEE Global Communications Conference, 2014
In this work, we consider the estimation of multiple jointly sparse vectors (or signals) from noisy, undetermined, linear measurements acquired by multiple nodes connected in a network. We propose a decentralized Bayesian algorithm, which is able to exploit the joint sparsity structure across the nodes.
Saurabh Khanna, Chandra R. Murthy
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Network Tomography via Sparse Bayesian Learning

IEEE Communications Letters, 2017
Network tomography aims to estimate internal link states from end-to-end path measurements. In this letter, we propose a new network tomography scheme using sparse Bayesian learning (SBL). SBL takes advantage of the sparsity of link-level parameters and implements Bayes’ rule based on the Gaussian Prior.
Xiaobo Fan, Xingming Li
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Robust Sparse Bayesian Learning for DOA

2023 31st European Signal Processing Conference (EUSIPCO), 2023
Christoph F. Mecklenbräuker, Peter Gerstoft, Esa Ollila, Yongsung Park   +3 more
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Bayesian Nonparametric Learning for Hierarchical and Sparse Topics

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018
This paper presents the Bayesian nonparametric (BNP) learning for hierarchical and sparse topics from natural language. Traditionally, the Indian buffet process provides the BNP prior on a binary matrix for an infinite latent feature model consisting of a flat layer of topics. The nested model paves an avenue to construct a tree model instead of a flat-
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