Titlebook: Low-Rank and Sparse Modeling for Visual Analysis; Yun Fu Book 2014 Springer International Publishing Switzerland 2014 Compressive Sensing.

我贊成

書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis影響因子(影響力)




書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis影響因子(影響力)學(xué)科排名




書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis被引頻次




書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis被引頻次學(xué)科排名




書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis年度引用




書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis年度引用學(xué)科排名




書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis讀者反饋




書(shū)目名稱Low-Rank and Sparse Modeling for Visual Analysis讀者反饋學(xué)科排名

Maximize

Latent Low-Rank Representation,x itself is chosen as the dictionary, resulting in a powerful method that is useful for both subspace clustering and error correction. However, such a strategy may depress the performance of LRR, especially when the observations are insufficient and/or grossly corrupted. In this chapter we therefore

NEX

Scalable Low-Rank Representation,under large-scale settings. In this chapter we therefore address the problem of solving nuclear norm regularized optimization problems (NNROPs), which contain a category of problems including LRR. Based on the fact that the optimal solution matrix to an NNROP is often low-rank, we revisit the classi

隼鷹

Density

Low-Rank Transfer Learning,beled data for the new task may save considerable labeling efforts. However, data in the auxiliary databases are often obtained under conditions that differ from those in the new task. Transfer learning provides techniques for transferring learned knowledge from a . domain?to a . domain by mitigatin

GNAW

Sparse Manifold Subspace Learning,ods considering global data structure e.g., PCA, LDA, SMSL aims at preserving the local neighborhood structure on the data manifold and provides a more accurate data representation via locality sparse coding. In addition, it removes the common concerns of many local structure based subspace learning

菊花

Low Rank Tensor Manifold Learning,s fact, two interesting questions naturally arise: How does the human brain represent these tensor perceptions in a “manifold” way, and how can they be recognized on the “manifold”? In this chapter, we present a supervised model to learn the intrinsic structure of the tensors embedded in a high dime

外觀

Crohns-disease

Low-Rank Outlier Detection,tion (SVDD) model. Different from the traditional SVDD, our approach learns multiple hyper-spheres to fit the normal data. The low-rank constraint helps us group the complicated dataset into several clusters dynamically. We present both primal and dual solutions to solve this problem, and provide th

septicemia