sparsity,英語單詞,主要用作名詞,作名詞時譯為“稀疏,稀少;貧乏”。
基本介紹
- 外文名:sparsity
- 詞性:名詞
- 英式發音:['spɑːsətɪ]
- 釋義:稀疏,稀少;貧乏

sparsity,英語單詞,主要用作名詞,作名詞時譯為“稀疏,稀少;貧乏”。
稀疏性,是指大多數信道係數的能量較小,而幾個能量較大的抽頭分布相隔較遠。術語解釋 "稀疏性" 英文 sparsity;光學偵察受到海洋氣候和氣象條件的限制,海洋環境具有目標稀疏性及機動性的影響。稀疏性限制 稀疏性可以被簡單地解釋如下。如果當神經元的輸出接近於1的時候我們認為它被激活,而輸出接近於0的時候認為它被...
1972年,Barlow推論出在稀疏性(Sparsity)和自然環境的統計特性之間必然存在某種聯繫.隨後,有許多計算方法被提出來論證這個推論,這些方法都成功地表明了稀疏表示可以體現出在大腦中出現的自然環境的統計特性。1987年,Field提出主視皮層V1區簡單細胞的感受野非常適於學習視網膜成像的圖像結構,因為它們可以產生圖像的稀疏表示....
sparse filtering的population sparsity和divisive normalization有著緊密的聯繫。divisive normalization是一種視覺處理過程,一個神經元的回響會除以其鄰域所有神經元的回響的和(或加權和)。divisive normalization在多級目標識別中是很有效的。然而,它被當成一個預處理階段引入,而不是屬於非監督學習(預訓練)的一部分。...
sparseness,英語單詞,主要用作名詞,作名詞時譯為“稀疏;稀少”。短語搭配 spectral sparseness 頻譜稀疏性 sparseness sparsity 稀疏 population sparseness 人口過疏 sparseness representation 稀疏表達 channel sparseness 信道的稀疏性 fuzzy sparseness 模糊稀疏度 data sparseness 數據稀疏 ; 數據稀疏問題 ; 料稀疏 ;...
《一種改進的稀疏度自適應匹配追蹤算法》是王福馳、趙志剛等撰寫的一篇論文。論文摘要 在信號稀疏度未知的情況下,稀疏度自適應匹配追蹤算法(Sparsity Adaptive Matching Pursuit,SAMP)是一種廣泛套用的壓縮感知重構算法。為了最佳化SAMP算法的性能,提出了一種改進的稀疏度自適應匹配追蹤(Improved Sparsity Adaptive Matching ...
磁共振圖像具有分段光滑的特性,通過k空間的隨機數據採集,能滿足CS對被採集信號稀疏性(Sparsity)和不連貫性(Incoherence)的要求。本項目開展了基於CS理論的快速核磁共振成像技術研究,完成了研究計畫要點:1、是用於MR 圖像的稀疏表示;2、建立了圖像稀疏變換不連貫性的量化準則;3、研究了適用於硬體的變密度k空間...
Guo, Guibing, Qiu Huihuai,Tan Zhenhua,Liu Yuan, Ma Jing, Wang Xingwei. Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems, Knowledge-Based Systems,(2017). DOI: 10.1016/j.knosys.2017.10.005 Zhenhua Tan,Jingyu Ning, Yuan Liu, Xingwei Wang, Guangming ...
4.Zhaocheng Yang, Rodrigo C. de Lamare, Weijian Liu, Sparsity-based STAP using alternating direction method with gain/phase errors, IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(6):2756-2768.5.Zhaocheng Yang, space-time adaptive processing by enforcing sparse constraint on ...
6.8Sparsity Techniques 301 6.9Fast Decoupled Power Flow 304 6.10The “DC” Power Flow 304 6.11Power Flow Modeling of Wind Generation 306 6.12Economic Dispatch 308 6.13Optimal Power Flow 321 ChAPTER 7 Symmetrical Faults 325 Case Study: Short-Circuit Modeling of a Wind Power Plant 326 7....
[61] Zhong, P-S, Chen, S. X. and Xu M. (2013). Tests alternative to higher criticism for high dimensional means under sparsity and column-wise dependence, Annals of Statistics, 41, 2820-2851.[62] Li, J. and S. X. Chen (2012). Two Sample Tests for High Dimensional Covariance ...
methods are quite different from direct methods such as Gaussianelimination. Direct methods based on an LU factorization of the matrix Aare prohibitive in terms of computing time and computer storage if A is quitelarge. Usually, in most large problems, the matrices are sparse. The sparsitymay ...
[6] Yang Yang, Zi Huang, Yi Yang, Heng Tao Shen and Jiebo Luo. “Local Image Tagging Via Graph Regularized Joint Group Sparsity”. Pattern Recognition (PR), 46(5): 1358-1368, 2013.[7] Yang Yang, Zi Huang, Heng Tao Shen and Xiaofang Zhou. “Mining Multi-tag Association for Image ...
1.4 Sparsity in Redundant Dictionaries 1.4.1 Frame Analysis and Synthesis 1.4.2 Ideal Iictionary Approximations 1.4.3 Pursuit in Dictionaries 1.5 Inverse Problems 1.5.1 Diagonal Inverse Estimation 1.5.2 Super-resolution and Compressive Sensing 1.6 Travel Guide 1.6.1 Reproducible ...
2. Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Jiachao Zhang and Ce Zhu, "A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization", IEEE Transactions on Image Processing (TIP), vol. 29, pp. 5094-5109, 2020. (中科院1區、CCF A)3. Zhiyuan Zha, Bihan Wen, Xin...
[34] Changpeng Wang, Jiangshe Zhang, Xueli Song. Tianjun Wu. Face clustering via learning a sparsity preserving low-rank grap. Multimedia Tools and Applications, 79: 29179–29198, 2020. (SCI)[33] 魯伊莎, 王凱明,肖玉柱,宋學力. 基於高斯近似l0範數典型相關性分析的特徵選擇. 計算機仿真,37(...
[4] Huang, J.,Wang, L., Chu, C., Zhang, Y., Liu, W. and Zhu, Y. Cardiac diffusion tensor imaging based on compressed sensing using joint sparsity and low-rank approximation. Technology and Health Care, 24(s2), S593-S599, 2016.[5] Hong-bo DU, Li-Hui WANG*, Wan-Yu LIU, ...
[3] Wang L.Q., Zhou H., Liu W. L., Yu B., Zhang S., 2021, High-resolution seismic acoustic impedance inversion with sparsity-based statistical model, Geophysics, 86(5), B237-V328.[4] Chen H., Zhou H., Rao Y. 2021, Constant-Q wave propagation and compensation by pseudo-...
smoothing. The first part discusses theoretical and practical issues concerned with minimum risk thresholding and fast threshold estimation, using generalized cross validation. The extensions in later chapters consider possibilities to exploit three key properties of wavelets in statistics: sparsity, multi...
Sparsity vs. Partial Separability 242 Group Partial Separability 243 9.6 Algorithms for Partially Separable Functions 244 Exploiting Partial Separability in Newton's Method 244 Quasi-Newton Methods for Partially Separable Functions 245 Notes and References 247 Exercises 248 10 Nonlinear Least-Squares ...
[6].Spectral CT reconstruction with image sparsity and spectral mean.IEEE Transactions on Computational Imaging, pp. 510-523, vol. 2, no. 4, 2016.[7].Fractional extreme value adaptive training method: fractional steepest descent approach. IEEE Transactions on Neural Networks and Learning Systems, ...
3.3 Regularization with Sparsity Constraints 3.4 Linear Inverse Problems with Convex Constraints 3.5 Schlieren Tomography.3.6 Further Literature on Regularization Methods for Inverse Problems 4 Convex Regularization Methods for Denoising 4.1 The Number 4.2 Characterization of Minimizers 4.3 One-...
Jian-wei Liu*, Li-peng Cui, Xiong-lin Luo. MCR SVM classifier with group sparsity. Optik - International Journal for Light and Electron Optics, 2016/09, 127(17): 6915-6926 Pengfei Cao*, Xionglin Luo. Performance analysis of multi-innovation stochastic Newton recursive algorithms. Digital ...
