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	Stable feature selection from brain sMRI
	Xin, Bo ; Huf, Lingjing ; Wang, Yizhou ; Gao, Wen
	2015
英文摘要	Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples. In this regard, sparse models, e.g. The lasso, are applied to select the optimal features and achieve high diagnosis accuracy. The lasso, however, usually results in independent unstable features. Stability, a manifest of reproducibility of statistical results subject to reasonable perturbations to data and the model (Yu 2013), is an important focus in statistics, especially in the analysis of high dimensional data. In this paper, we explore a nonnegative generalized fused lasso model for stable feature selection in the diagnosis of Alzheimer's disease. In addition to sparsity, our model incorporates two important pathological priors: the spatial cohesion of lesion voxels and the positive correlation between the features and the disease labels. To optimize the model, we propose an efficient algorithm by proving a novel link between total variation and fast network flow algorithms via conic duality. Experiments show that the proposed nonnegative model performs much better in exploring the intrinsic structure of data via selecting stable features compared with other state-of-the-arts. ? Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; EI; 1910-1916; 3
语种	中文
出处	29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436821]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Xin, Bo,Huf, Lingjing,Wang, Yizhou,et al. Stable feature selection from brain sMRI. 2015-01-01.

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