Feng, W. Hu, Predicting missing markers in human motion capture using l1-sparse representation, Comput. Animat. Virt. Worlds 22 (2-3) (2011) 221-228.Xiao, J., Feng, Y., Hu, W.: Predicting missing markers in human motion capture using l1-sparse representation. Computer Animation and ...
l1-norm sparse Bayesian learning: Theory and applications The elements in the real world are often connected. For example, in a social network, each individual is only sparsely connected to a small portion of peop... Y Lin - University of Pennsylvania 被引量: 24发表: 2008年 L(1)-norm ...
MSELoss对于稀疏数据仍然按照常规的方式进行计算,而L1Loss则更加敏感地处理稀疏数据。这意味着在使用PyTorch Sparse进行训练时,如果使用MSELoss,可能会浪费大量的计算资源和存储空间;而使用L1Loss则可以更好地利用稀疏数据的优势。四、总结MSELoss和L1Loss是两种常用的损失函数,它们在计算方式、梯度传播以及对稀疏数据的处...
Xu, "Improve robustness of sparse PCA by L1-norm maximization," Pattern Recognit., vol. 45, no. 1, pp. 487-497, Jan. 2012.D. Meng, Q. Zhao, and Z. Xu, "Improve robustness of sparse PCA by L1-norm maximization," Patt. Recog., vol. 45, pp. 487-497, Jan. 2012....
spgl1.m Remove docs Dec 17, 2019 spgsetup.m Correctly catch mex compile errors. Jun 9, 2020 README LGPL-2.1 license SPGL1: A solver for large-scale sparse least squares Documentation:https://friedlander.io/spgl1 Introduction SPGL1 is a Matlab solver for large-scale one-norm regularized le...
The l1/l2 ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works. Indeed, it benefits from a scale invariance property much desirable in the blind context. However, the l1/l2 function raises some difficulties when solving the nonconvex ...
We discuss the properties of sparse approximation using l(1)-l(2) minimization. We present several theoretical estimates regarding its recoverability for both sparse and nonsparse signals. We then apply the method to sparse orthogonal polynomial approximations for stochastic collocation, with a focus ...
A variety of practical methods have recently been introduced for finding maximally sparse representations from overcomplete dictionaries, a central computational task in compressive sensing applications as well as numerous others. Many of the underlying algorithms rely on iterative reweighting schemes ...
In this paper, we propose a sparse multinomial logistic regression method, in which the sparsity arises from the use of a Laplace prior, but where the usual regularisation parameter is integrated out analytically. Evaluation over a range of benchmark datasets reveals this approach results in ...
Sparse reconstruction was performed by solving the basis pursuit problem using SPGL1 package. The global impedance data (i.e. sum of all 544 measurements... A Shiraz,D Khodadad,S Nordebo,... - 《Physiological Measurement》 被引量: 0发表: 2019年 基于稀疏表示的地震数据重建方法研究 (Gradient ...