Xiaonan Song

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This paper presents a novel tensor-based feature learning approach for whole-brain fMRI classification. Whole-brain fMRI data have high exploratory power, but they are challenging to deal with due to large numbers of voxels. A critical step for fMRI classification is dimensionality reduction, via feature selection or feature extraction. Most current(More)
To investigate the association of single nucleotide polymorphism in the matrix metalloproteinase-3 (MMP3) gene promoter with the susceptibility to the middle cerebral artery stenosis. A case–control study was performed by determining the genotype of MMP3 gene promoter region using polymerase chain reaction-restriction fragment length polymorphism in 119(More)
Hierarchical structures are a common organization of data in the real world. In this paper, we propose a new type of tree structures called diamond tree to present the organization and relationship of hierarchical data. We design a spatial layout using the geometric theory, starting with a simple design and continually improving until the design that makes(More)
Feature selection is an important step for large-scale image data analysis, which has been proved to be difficult due to large size in both dimensions and samples. Feature selection firstly eliminates redundant and irrelevant features and then chooses a subset of features that performs as efficient as the complete set. Generally, supervised feature(More)
Embedded feature selection is effective when both prediction and interpretation are needed. The Lasso and its extensions are standard methods for selecting a subset of features while optimizing a prediction function. In this paper, we are interested in embedded feature selection for multidimensional data, wherein (1) there is no need to reshape the(More)
Right-to-left shunt (RLS) is associated with cryptogenic stroke and migraine. Herein we investigated the relationship between RLS and silent lacunar infarcts in patients with migraine. A total of 263 patients with migraine who met eligibility criteria were enrolled from January 2010 to December 2011, among which 127 subjects fell into RLS group. Baseline(More)
In this paper, we propose an adaptive higher-order CRF (HCRF) labeling approach towards automatic video object segmentation with better performances, higher effectiveness and efficiency. In comparison with the existing state of the arts, our approach achieves further improvements in terms of segmentation results. Our contribution can be highlighted as: (i)(More)
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