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Description: 算法实现:Jieping Ye. Generalized low rank approximations of matrices. Machine Learning, Vol. 61, pp. 167-191, 2005. -Algorithm : Jieping Ye. Generalized low rank approximatio ns of matrices. Machine Learning, Vol. 61. pp. 167-191, 2005.
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Size: 1050 |
Author: lxm |
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Description: Gabor-function convolution masks are increasingly used in image processing and computer vision. This function simply computes the cosine and sine masks for a given width, period and orientation. The masks returned are properly normalised.
It is not highly optimised - e.g. the symmetries are not used to reduce the computation.
It is recommended that convolutions with these masks are done using CONVOLVE2 (available from Matlab File Exchange) to take advantage of their low rank.
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Size: 1637 |
Author: li |
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Description:
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Size: 354254 |
Author: tangelx |
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Description: robust recovery of subspace structures by low rank representation
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Size: 354523 |
Author: googleoy |
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Description: 算法实现:Jieping Ye. Generalized low rank approximations of matrices. Machine Learning, Vol. 61, pp. 167-191, 2005. -Algorithm : Jieping Ye. Generalized low rank approximatio ns of matrices. Machine Learning, Vol. 61. pp. 167-191, 2005.
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Size: 1024 |
Author: lxm |
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Description: Gabor-function convolution masks are increasingly used in image processing and computer vision. This function simply computes the cosine and sine masks for a given width, period and orientation. The masks returned are properly normalised.
It is not highly optimised - e.g. the symmetries are not used to reduce the computation.
It is recommended that convolutions with these masks are done using CONVOLVE2 (available from Matlab File Exchange) to take advantage of their low rank.
-Gabor-function convolution masks are increasingly used in image processing and computer vision. This function simply computes the cosine and sine masks for a given width, period and orientation. The masks returned are properly normalised.
It is not highly optimised- e.g. the symmetries are not used to reduce the computation.
It is recommended that convolutions with these masks are done using CONVOLVE2 (available from Matlab File Exchange) to take advantage of their low rank.
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Size: 1024 |
Author: li |
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Description: 这是学生的成绩从高到低进行排列的C程序,很实用的-This is the scores of pupils from high to low rank of C procedures, a very practical
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Size: 3072 |
Author: 何蓉 |
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Description: pca: The enclosed function PCA implements what is probably the method of choice for computing principal component analyses fairly efficiently, while guaranteeing nearly optimal accuracy. The enclosed function DIFFSNORM provides an efficient, reliable means for checking the accuracies of the low-rank approximations produced by PCA (often the accuracies are slightly better than recently proven bounds guarantee).
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Size: 5120 |
Author: zhangxq |
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Description: 递秩矩阵拟合，Matrix Completion ，Sparse Matrix Separation ，Matrix Compressive Sensing -Low-rank Matrix Fitting
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Size: 19456 |
Author: 袁鑫 |
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Description: Manuscript for detailing the method of low-rank representation
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Size: 283648 |
Author: G. Liu |
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Description: Matlab code to run "Robust subsapce segmentation by low-rank representation"
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Size: 7168 |
Author: G. Liu |
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Description: 低秩的求解 denoise an image by sparsely representing each block with the
already overcomplete trained Dictionary, and averaging the represented parts.
Detailed description can be found in "Image Denoising Via Sparse and Redundant
representations over Learned Dictionaries-low rankdenoise an image by sparsely representing each block with the
already overcomplete trained Dictionary, and averaging the represented parts.
Detailed description can be found in "Image Denoising Via Sparse and Redundant
representations over Learned Dictionaries
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Size: 2108416 |
Author: 中和 |
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Description: 稀疏和低秩矩阵分解。
This paper focuses on the algorithmic improvement for the sparse and low-rank recovery.- Sparse and Low-Rank Matrix Decomposition Via Alternating Direction Methods.The problem of recovering the sparse and low-rank components of a matrix captures a broad spectrum of applications.
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Size: 210944 |
Author: 飒飒 |
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Description: 一种新的稀疏矩阵，低秩矩阵的处理方法,推荐一下-A new sparse matrix, a low rank matrix approach, recommend
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Size: 500736 |
Author: citronnelle |
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Description: Low-rank optimization for distance matrix completion
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Size: 183296 |
Author: M.Hasani |
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Description: 低秩子空间聚类，用于图像分割聚类，能解决图像去噪等问题-Low rank subspace clustering
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Size: 6144 |
Author: lizelong |
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Description: 低秩子空间聚类。来源是Favaro在CVPR11年发表的一篇论文。-Low rank subspace clustering.
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Size: 5120 |
Author: 王振 |
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Description: 可实现闭式解低秩子空间聚类，该程序特点：收敛速度较快，但是有多个参数需要调整。参考文献：Rene Vidal, Paolo Favaro. Low rank subspace clustering (LRSC) [J]. Pattern Recognition Letters, 2014, 43: 47-61.-This program can realize closed-form low rank subspace clustering. The characteristic of the program: the convergence speed is fast while there are many parameters needed to be adjusted. Reference: Rene Vidal, Paolo Favaro. Low rank subspace clustering (LRSC) [J]. Pattern Recognition Letters, 2014, 43: 47-61.
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Size: 10240 |
Author: 宋昱 |
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Description: 2017 TGRS 最新去噪文章
Image Denoising Using Local Low-Rank-2017 TGRS Latest articles Denoising
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Size: 4925440 |
Author: degawong |
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Description: 在文中，提出来一个基于低秩的特征提取方法(Feature extraction plays a significant role in pattern
recognition. Recently, many representation-based feature extraction methods have been proposed and achieved successes in many
applications. As an excellent unsupervised feature extraction
method, latent low-rank representation (LatLRR) has shown
its power in extracting salient features)
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Size: 1943552 |
Author: HUAZI123 |
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