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- Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank
- IEEE Transactions on Pattern Analysis and Machine…
- 2007

Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we… (More)

- Dacheng Tao, Xiaoou Tang, Xuelong Li, Xindong Wu
- IEEE Transactions on Pattern Analysis and Machine…
- 2006

Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training… (More)

- Tianhao Zhang, Dacheng Tao, Xuelong Li, Jie Yang
- IEEE Transactions on Knowledge and Data…
- 2009

Spectral analysis-based dimensionality reduction algorithms are important and have been popularly applied in data mining and computer vision applications. To date many algorithms have been developed, e.g., principal component analysis, locally linear embedding, Laplacian eigenmaps, and local tangent space alignment. All of these algorithms have been… (More)

- Xinbo Gao, Bing Xiao, Dacheng Tao, Xuelong Li
- Pattern Analysis and Applications
- 2008

Inexact graph matching has been one of the significant research foci in the area of pattern analysis. As an important way to measure the similarity between pairwise graphs error-tolerantly, graph edit distance (GED) is the base of inexact graph matching. The research advance of GED is surveyed in order to provide a review of the existing literatures and… (More)

- 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)

- Han Hu, Yonggang Wen, Tat-Seng Chua, Xuelong Li
- IEEE Access
- 2014

- Dacheng Tao, Xuelong Li, Weiming Hu, Stephen J. Maybank, Xindong Wu
- Knowledge and Information Systems
- 2005

Tensor representation is helpful to reduce the small sample size problem in discriminative subspace selection. As pointed by this paper, this is mainly because the structure information of objects in computer vision research is a reasonable constraint to reduce the number of unknown parameters used to represent a learning model. Therefore, we apply this… (More)

- Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank
- IEEE Transactions on Pattern Analysis and Machine…
- 2009

Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information… (More)

- Yanwei Pang, Yuan Yuan, Xuelong Li
- IEEE Trans. Circuits Syst. Video Techn.
- 2008

- Xiaoqiang Lu, Hao Wu, Yuan Yuan, Pingkun Yan, Xuelong Li
- IEEE Trans. Geoscience and Remote Sensing
- 2013

Hyperspectral unmixing is one of the most important techniques in analyzing hyperspectral images, which decomposes a mixed pixel into a collection of constituent materials weighted by their proportions. Recently, many sparse nonnegative matrix factorization (NMF) algorithms have achieved advanced performance for hyperspectral unmixing because they overcome… (More)