Xu Lu-zhou, Zhao Ke-xin, Li Jian, et al.. Wideband source localization using sparse learning via iterative minimization [J]. Signal Processing, 2013, 93(12): 3504-3514.L. Xu, K. Zhao, J. Li, and P. Stoica, "Wideband source localization using sparse 354 learning via iterative ...
In this paper, we present a regularized minimization approach to sparse signal recovery. Sparse learning via iterative minimization (SLIM) follows an lq-norm constraint (for 0 <; q ≤ 1), and can thus be used to provide more accurate estimates compared to the l1-norm based approaches. We ...
Representation recovery via L1-norm minimization with corrupted data Information Sciences, Volume 595, 2022, pp. 395-426 Woon Huei Chai,…, Hiok Chai Quek Gradient projection Newton algorithm for sparse collaborative learning using synthetic and real datasets of applications Journal of Computational and...
The proposed approach is compared with six state-of-the-art tracking methods including incremental visual tracking (IVT) method [18], fragment-based (FragTrack) tracking method [1], L-1 tracker(L-1)[16], multiple instance learning (MIL) tracker [3], visual tracking decomposition (VTD) meth...
We proposed using a sparse, quantized neural code to deal with noisy and partial inputs and to prevent catastrophic forgetting, and implementing this strategy via a discrete graphical model that performed MAP learning, an algorithm that uses local learning rules. We implemented this approach in the...
C. Homotopy(ITERATIVE REWEIGHTING l1-NORM MINIMIZATION VIA HOMOTOPY) D. Homotopy(OTHER HOMOTOPY ALGORITHMS FOR SPARSE REPRESENTATION) VIII. 稀疏表示的应用(THE APPLICATIONS OF THE SPARSE REPRESENTATION METHOD) A. 字典学习中的稀疏表示(SPARSE REPRESENTATION IN DICTIONARY LEARNING) ...
稀疏贝叶斯学习(SparseBayesianLearning) 稀疏贝叶斯学习(Sparse Bayesian Learning) 张智林(Zhilin?Zhang)? z4zhang@ Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093-0407, USA 1 引言 稀疏贝叶斯学习(Sparse Bayesian Learning, SBL)最初作为一种机器...
From this starting point, dictionary learning is performed using an objective function balancing (1) approximation error, (2) smoothness over a nearest-neighbor graph of genes, and (3) smoothness over a nearest-neighbor graph of cell contexts. This is performed via dual-graph-regularized k-SVD ...
Due to the property of the beta process that the sparse representation can be decomposed to values and sparsity indicators, the proposed algorithm ingeniously captures the temporal correlation structure by the learning of amplitudes and the spatial correlation structure by the estimation of clustered ...
This method could achieve superior performance of clutter suppression for a conformal array. Simulation results demonstrate the effectiveness of this method. Keywords: space-time adaptive processing; sparse learning via iterative minimization; Laplace prior; clutter suppression; conformal array...