Hou Z G; Wang C; Yin H C.Multilevel thresholding method for a sparse representation of reduced matrix in CBFM.Journal of Electromagnic Waves and Applieations.2010.2605-2614Hou Z G, Wang C, Yin H C. Multilevel t
Sparse Subspace Clustering with Jointly Learning Representation and Affinity Matrix In recent years, sparse subspace clustering (SSC) has been witnessed to its advantages in subspace clustering field. Generally, the SSC first learns the representation matrix of data by self-expressive , and then constru...
Specifically, this model downscales DB10K AOD based on a resolved sparse representation matrix of a learned overcomplete AOD dictionary pair (10 km and 3 km), which introduces the spatial information from DT3K AOD and the spatial coverage from DB10K AOD. To validate the fused 3 km AOD ...
in a nanoscale crossbar array. This computing scheme enables the parallel reading of stored data and the one-shot operation of matrix–matrix multiplications in the crossbar array. Furthermore, we achieve the one-shot recognition of 16 letter images based on two physically interconnected crossbar ...
6). On the basis of more comprehensive evaluation metrics (including accuracy, AUC, AUPR—area under the precision–recall curve, F1 score, precision, recall, kappa and confusion matrix), we found that ImageMol is able to achieve high performance across these metrics (Supplementary Tables 7 and...
The first challenge is to define a data structure that combines the time-frequency characteristics of the EEG signal with functional brain connectivity. So far, a wide range of methods have been developed to solve the EEG classification problems in various circumstances. The sparse support matrix ...
E. 基于 l1/2-norm 正则化的稀疏重建( l1/2-NORM REGULARIZATION BASED SPARSE REPRESENTATION) 带有lp-norm (0 < p < 1) 正则化的稀疏表示 通常是 一个 nonconvex, nonsmooth, 和 non-Lipschitz 优化问题。但是, 大多数有代表性的有关 lp-norm (0 < p < 1) 正则化 的算法是带有 l1/2-norm 正则...
difficulties in the detection of video anomalies involving multiple objects and secondly, the capability of the sparsity-based linear model to represent the class separation effectively. These problems are addressed with the help of adaptivesparse representations, i.e., a combination of jointsparsitymode...
matrix-inversionfreeprocedure. IndexTerms—Sparserepresentation,nonparametric methods,dictionarylearning I.INTRODUCTION Sparserepresentationofsignalsoverovercomplete dictionariesshowsstate-of-artresultsinsignalprocessing, compression,andfeatureextraction[1],[2].Suppose m yand k xaretheinputsignalandthecoefficient vecto...
Sparse representation and inversion have been widely used in the acquisition and processing of geophysical data. In particular, the low-rank representation of seismic signals shows that they can be determined by a few elementary modes with predominantly large singular values. We review global and loca...