"Tensor Completion via Nonlocal Low-Rank Regularization"介绍了一种基于非局部低秩正则化的张量补全方法。该方法通过利用张量中的非局部结构信息,结合低秩正则化技术,实现了高效的张量补全。作者提出的方法在处理具有缺失值的张量数据时表现出良好的效果,并在实验中取得了较好的结果。该研究对于解决张量数据的缺失值...
Second, we introduce an intra-modal low-rank regularization, which encourages the intra-class samples that originate from the same space to be more relevant in the common feature space. Third, an inter-modal low-rank regularization is applied to reduce the cross-modal discrepancy. To enable the...
Low Rank Regularization (LRR), in essence, involves introducing a low rank or approximately low rank assumption to target we aim to learn, which has achieved great success in many data analysis tasks. Over the last decade, much progress has been made in theories and applications. Nevertheless,...
A CNN channel pruning low-bit framework using weight quantization with sparse group lasso regularization The deployment of large-scale Convolutional Neural Networks (CNNs) in limited-power devices is hindered by their high computation cost and storage. In this... Long, XinZeng, XiangrongLiu, YanXi...
超参数的数量随着网络层数的增加呈线性变化趋势,例如中间层的特征通道数等等。参考2016年的NIPSConvolutional neural networks with low-rank regularization 随着模型复杂度的提升,搜索空间急剧增大——Learning structured sparsity in deep neural networks
Snapshot compressive imaging Video processing Nonconvex function Low-rank regularization 1. Introduction Multi-dimensional high-resolution data, such as high-speed videos and hyperspectral images, have recently become ubiquitous in artificial intelligence and robotics [1], [2]. It is well known that ...
来源期刊 IEEE Transactions on Geoscience and Remote Sensing 研究点推荐 Nonlocal Low-Rank Regularized Tensor Decomposition Hyperspectral image (HSI) Hyperspectral Image Denoising CANDECOMP/PARAFAC (CP) tensor decomposition (CPTD) nonlocal low-rank regularization (LR) 站内活动 0...
LORAKS was based on the observation that certain matrices constructed from fully sampled k-space data should have low rank whenever the image has limited support or smooth phase, and made it possible to accurately reconstruct images from undersampled or noisy data using low-rank regularization. ...
To integrate the global and non-local property of the underlying tensor, we propose a novel low-rank tensor completion model via combined non-local self-similarity and low-rank regularization, which is named as NLS-LR. We adopt the parallel low-rank matrix factorization to guarantee the global...
Super-resolutionLow rankFusionRegularizationExample-learning-based algorithms such as those based on sparse coding or neighbor embedding have been popular for single image super-resolution in recent years. However, affected by several critical...