Ming Xiang
Xi'an Jiaotong University
6 Papers
3 Citations
Ming Xiang is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Dimensionality reduction & Sparse approximation. The author has an hindex of 5, co-authored 6 publications.
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Papers
Low-rank preserving embedding
Yupei Zhang,Ming Xiang,Bo Yang +2 more
TL;DR: This paper proposes a new linear dimensionality reduction method by virtue of the lowest rank representation (LRR) of data, which is dubbed low-rank preserving embedding (LRPE), which achieves all data self-representations jointly and can thus extract the global structure of a data set as a whole.
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Linear dimensionality reduction based on Hybrid structure preserving projections
Yupei Zhang,Ming Xiang,Bo Yang +2 more
TL;DR: The Sparsity and Neighborhood Preserving Projections, dubbed SNPP, is proposed by taking both of them into account and proposed two integrated approaches for dimensionality reduction and roughly draws the conclusion that neighborhood structure is more important for low-dimensional data while sparsity structure isMore useful for high- dimensional data.
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Graph regularized nonnegative sparse coding using incoherent dictionary for approximate nearest neighbor search
Yupei Zhang,Ming Xiang,Bo Yang +2 more
TL;DR: This work introduces the graph Laplacian regularization to preserve the local structure of the original data into reduced space, which is indeed beneficial to ANN, and imposes non-negativity constraints such that the retrieval code can more effectively reflect the neighborhood relation, thereby cutting down on unnecessary hash collision.
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Hierarchical sparse coding from a Bayesian perspective
Yupei Zhang,Ming Xiang,Bo Yang +2 more
TL;DR: This paper reformulate the hierarchical sparse model from a Bayesian perspective employing twofold priors: the spike-and-slab prior and the Laplacian prior, and proposes a nest prior by integrating the both priors to result in hierarchical sparsity.
10
Learning discriminant isomap for dimensionality reduction
Bo Yang,Ming Xiang,Yupei Zhang +2 more
- 12 Jul 2015
TL;DR: A new supervised manifold learning method namely discriminant Isomap (D-Isomap) is proposed, in which the geometrical structure of each class data is preserved by keeping geodesic distances between data points of the same class and the discriminant capacity is enhanced by maximizing the distances betweenData points of different classes.
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