通过优化方法解析散射变换特征的分类意义，揭示模型决策依据。
Explainable and Class-Revealing Signal Feature Extraction via Scattering Transform and Constrained Zeroth-Order Optimization
- 结合散射变换与逻辑回归，用零阶优化寻找使类别概率最大化的输入模式。
- 在合成时间序列上验证，可解释特征与高分类准确率并存。
- 适合关注模型可解释性与特征生成机制的研究者。
我们提出一种新方法，从特定机器学习模型（即散射变换与多类逻辑回归的组合）中提取判别性强且可解释的特征。尽管该模型以高分类率识别多种信号类别而闻名，但其成功背后的机理仍不清晰，主要源于散射变换的非线性特性。为揭示多类逻辑回归（采用Lasso正则化）所选散射系数的意义，我们采用零阶优化算法，搜索在已学习模型下使目标类别概率最大的输入模式。研究发现，对输入模式施加稀疏性与平滑性约束至关重要。我们在多个合成时间序列分类问题上验证了该方法的有效性。
原文摘要 · Abstract (English)
We propose a new method to extract discriminant and explainable features from a particular machine learning model, i.e., a combination of the scattering transform and the multiclass logistic regression. Although this model is well-known for its ability to learn various signal classes with high classification rate, it remains elusive to understand why it can generate such successful classification, mainly due to the nonlinearity of the scattering transform. In order to uncover the meaning of the scattering transform coefficients selected by the multiclass logistic regression (with the Lasso penalty), we adopt zeroth-order optimization algorithms to search an input pattern that maximizes the class probability of a class of interest given the learned model. In order to do so, it turns out that imposing sparsity and smoothness of input patterns is important. We demonstrate the effectiveness of our proposed method using a couple of synthetic time-series classification problems.
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