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- Yao Hu, Debing Zhang, Jieping Ye, Xuelong Li, Xiaofei He
- IEEE Transactions on Pattern Analysis and Machine…
- 2013

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 approximation problem. Since the rank operator is nonconvex and discontinuous, most of the recent theoretical… (More)

- Debing Zhang, Yao Hu, Jieping Ye, Xuelong Li, Xiaofei He
- 2012 IEEE Conference on Computer Vision and…
- 2012

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, by minimizing the nuclear norm, all the singular values are simultaneously minimized, and thus the rank… (More)

- David Mendonça, Yao Hu
- 2008

Recent research has argued forcefully and persuasively that to understand creative thinking it is necessary to investigate both convergent and divergent thinking processes. In the context of group decision making in emergency response, the link between these processes is particularly relevant, since stakes are high and, by definition, thinking must conclude… (More)

- Zhongming Jin, Yao Hu, +4 authors Xuelong Li
- 2013 IEEE International Conference on Computer…
- 2013

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 method should satisfy the following two requirements: 1) mapping the nearby data points into the same… (More)

- Yao Hu, Zhongming Jin, Hongyi Ren, Deng Cai, Xiaofei He
- ACM Multimedia
- 2014

Cross media retrieval engines have gained massive popularity with rapid development of the Internet. Users may perform queries in a corpus consisting of audio, video, and textual information. To make such systems practically possible for large mount of multimedia data, two critical issues must be carefully considered: (a) reduce the storage as much as… (More)

- Pierre Dillenbourg, Armin Weinberger, +7 authors Naomi Miyake
- 2005

- Debing Zhang, Genmao Yang, Yao Hu, Zhongming Jin, Deng Cai, Xiaofei He
- IJCAI
- 2013

Nowadays, Nearest Neighbor Search becomes more and more important when facing the challenge of big data. Traditionally, to solve this problem , researchers mainly focus on building effective data structures such as hierarchical k-means tree or using hashing methods to accelerate the query process. In this paper, we propose a novel unified approximate… (More)

In many real world scenarios, active learning methods are used to select the most informative points for labeling to reduce the expensive human action. One direction for active learning is selecting the most representative points, ie., selecting the points that other points can be approximated by linear combination of the selected points. However, these… (More)

- Yao Hu, Debing Zhang, Jun Liu, Jieping Ye, Xiaofei He
- KDD
- 2012

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 Singular Value Thresholding (SVT) algorithm is a simple and efficient first-order matrix completion… (More)

- Zhongming Jin, Debing Zhang, Yao Hu, Shiding Lin, Deng Cai, Xiaofei He
- IEEE Trans. Cybernetics
- 2014

Recently, the hashing techniques have been widely applied to approximate the nearest neighbor search problem in many real applications. The basic idea of these approaches is to generate binary codes for data points which can preserve the similarity between any two of them. Given a query, instead of performing a linear scan of the entire data base, the… (More)