Randomized Structural Sparsity-Based Support Identification with Applications to Locating Activated or Discriminative Brain Areas: A Multicenter Reproducibility Study

Wang, Yilun; Zhang, Sheng; Zheng, Junjie; Chen, Heng; Chen, Huafu

doi:10.1109/tamd.2015.2427341

Cited by 3 publications

(2 citation statements)

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“…The existing applications are limited in quantity, but rich in diversity from the following aspects. First of all, in current studies, stability selection has been used to satisfy a variety of research purposes such as exploring the brain functionality in visual pathways [ 11 ], functional networks [ 13 , 14 ], resting-state networks [ 15 ], or the localization (or identification) of significant biomarkers relevant to specific mental states [ 16 ] or diagnose brain-related disorders [ 17 ]. Second, in terms of methodology, numerous variations were made utilizing the concept of stability selection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A novel variation of LASSO was proposed to search for similar but not identical voxels in feature selection across multiple human subjects [ 22 ]. Finally, although the original stability selection was proposed as a data-driven model, some novel methods also utilized anatomical information, topological structure, or other structural information underlying features to enhance its stability and predictive power [ 16 , 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study

Fan

Chou

2016

Brain Inf.

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Feature selection plays a key role in multi-voxel pattern analysis because functional magnetic resonance imaging data are typically noisy, sparse, and high-dimensional. Although the conventional evaluation criterion is the classification accuracy, selecting a stable feature set that is not sensitive to the variance in dataset may provide more scientific insights. In this study, we aim to investigate the stability of feature selection methods and test the stability-based feature selection scheme on two benchmark datasets. Top-k feature selection with a ranking score of mutual information and correlation, recursive feature elimination integrated with support vector machine, and L1 and L2-norm regularizations were adapted to a bootstrapped stability selection framework, and the selected algorithms were compared based on both accuracy and stability scores. The results indicate that regularization-based methods are generally more stable in StarPlus dataset, but in Haxby dataset they failed to perform as well as others.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study

Fan

Chou

2016

Brain Inf.

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Multi-stage multi-task feature learning via adaptive threshold

Fan

Wang

Huang

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Multi-task feature learning aims to identity the shared features among tasks to improve generalization. It has been shown that by minimizing non-convex learning models, a better solution than the convex alternatives can be obtained. Therefore, a non-convex model based on the capped-ℓ 1 , ℓ 1 regularization was proposed in [1], and a corresponding efficient multi-stage multi-task feature learning algorithm (MSMTFL) was presented. However, this algorithm harnesses a prescribed fixed threshold in the definition of the capped-ℓ 1 , ℓ 1 regularization and the lack of adaptivity might result in suboptimal performance. In this paper we propose to employ an adaptive threshold in the capped-ℓ 1 , ℓ 1 regularized formulation, where the corresponding variant of MSMTFL will incorporate an additional component to adaptively determine the threshold value. This variant is expected to achieve a better feature selection performance over the original MSMTFL algorithm. In particular, the embedded adaptive threshold component comes from our previously proposed iterative support detection (ISD) method [2]. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of this new variant over the original MSMTFL.

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Guest Editorial Multimodal Modeling and Analysis Informed by Brain Imaging—Part II

Han

Liu

Guo

et al. 2015

IEEE Trans. Auton. Mental Dev.

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Randomized Structural Sparsity-Based Support Identification with Applications to Locating Activated or Discriminative Brain Areas: A Multicenter Reproducibility Study

Abstract: Abstract-The abstract goes here.

Cited by 3 publications

References 75 publications

Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study

Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study

Multi-stage multi-task feature learning via adaptive threshold

Guest Editorial Multimodal Modeling and Analysis Informed by Brain Imaging—Part II

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