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Displaying 1-4 out of 4 total
Complementary Projection Hashing
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Zhongming Jin,Yao Hu,Yue Lin,Debing Zhang,Shiding Lin,Deng Cai,Xuelong Li
Issue Date:December 2013
pp. 257-264
Recently, hashing techniques have been widely applied to solve the approximate nearest neighbors search problem in many vision applications. Generally, these hashing approaches generate 2^c buckets, where c is the length of the hash code. A good hashing me...
 
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Yao Hu, Debing Zhang, Jieping Ye, Xuelong Li, Xiaofei He
Issue Date:September 2013
pp. 2117-2130
Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real applications, such as image inpainting and recommender systems. Many existing approaches formulate this problem as a general low-rank matrix approxim...
 
Matrix completion by Truncated Nuclear Norm Regularization
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Debing Zhang, Yao Hu, Jieping Ye, Xuelong Li, Xiaofei He
Issue Date:June 2012
pp. 2192-2199
Estimating missing values in visual data is a challenging problem in computer vision, which can be considered as a low rank matrix approximation problem. Most of the recent studies use the nuclear norm as a convex relaxation of the rank operator. However, ...
 
Accelerated singular value thresholding for matrix completion
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Debing Zhang, Jieping Ye, Jun Liu, Xiaofei He, Yao Hu
Issue Date:August 2012
pp. 298-306
Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real world applications, such as recommender system and image in-painting. These problems can be formulated as a general matrix completion problem. The Si...
     
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