Sparsity Is Better with Stability: Combining Accuracy and Stability for Model Selection in Brain Decoding.
Sparsity Is Better with Stability: Combining Accuracy and Stability for Model Selection in Brain Decoding.
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DOI：
10.3389/fnins.2017.00062
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发表时间：
2017
影响因子：
4.3
通讯作者：
Mourão-Miranda J
中科院分区：
文献类型：
--
作者：
Baldassarre L;Pontil M;Mourão-Miranda J
结构化稀疏方法在神经影像学研究中受到广泛关注。这些方法允许通过预测模型中额外的空间和时间约束来整合领域知识，并且比非结构化稀疏方法（如LASSO或Elastic Net方法）更具可解释性。然而，尽管稀疏性经常被提倡为导致更可解释的模型，但它也可能导致在次采样或实验条件的微小变化下模型不稳定。在目前的工作中，我们研究了使用稳定性/可重复性作为额外的模型选择标准1对最近应用于fMRI脑解码的几种不同的稀疏（和结构化稀疏）方法的影响。我们比较了三种不同的模型选择标准：(i)单独的分类精度；（ii）分类精度和解决方案之间的重叠；（iii）分类精度和解之间的相关性。我们考虑的方法包括LASSO、弹性网、全变分、稀疏全变分、拉普拉斯和图拉普拉斯弹性网（GraphNET）。我们的研究结果表明，在模型优化过程中明确考虑稳定性/再现性可以减轻稀疏方法固有的一些不稳定性。特别是，使用精度和解之间的重叠作为联合优化准则，即使考虑不同的稀疏度方法，也可以得到在精度、稀疏度级别和系数映射方面更相似的解。
Structured sparse methods have received significant attention in neuroimaging. These methods allow the incorporation of domain knowledge through additional spatial and temporal constraints in the predictive model and carry the promise of being more interpretable than non-structured sparse methods, such as LASSO or Elastic Net methods. However, although sparsity has often been advocated as leading to more interpretable models it can also lead to unstable models under subsampling or slight changes of the experimental conditions. In the present work we investigate the impact of using stability/reproducibility as an additional model selection criterion1 on several different sparse (and structured sparse) methods that have been recently applied for fMRI brain decoding. We compare three different model selection criteria: (i) classification accuracy alone; (ii) classification accuracy and overlap between the solutions; (iii) classification accuracy and correlation between the solutions. The methods we consider include LASSO, Elastic Net, Total Variation, sparse Total Variation, Laplacian and Graph Laplacian Elastic Net (GraphNET). Our results show that explicitly accounting for stability/reproducibility during the model optimization can mitigate some of the instability inherent in sparse methods. In particular, using accuracy and overlap between the solutions as a joint optimization criterion can lead to solutions that are more similar in terms of accuracy, sparsity levels and coefficient maps even when different sparsity methods are considered.