提出新独立性准则，让降维兼具判别力、多样性与可解释性。
Beyond Correlation: Learning Supervised, Sample-Distinct, and Eigenimage-Interpretable Representations
- 基于统计独立性设计新降维准则，融合线性与非线性形式。
- 在手写数字和人脸性别数据集上，对比度提升20.1%，准确率提高17.4%。
- 特征可解释性强，适合需要可视化分析的场景，如医学图像或人脸识别。
传统降维方法主要优化方差或相关性，未能充分考虑统计依赖性、数据多样性、对比度及可解释性。本文提出三种新的独立性准则，用于设计有监督与无监督降维方法，旨在提升特征提取与表示质量。该框架结合线性与非线性形式，并通过对比度、分类准确率与可解释性指标进行评估。特征空间中特征脸（eigenfaces）的可解释性有助于有效总结代表性图像中的类特定结构与趋势。在MNIST与性别人脸数据集上的实验表明，相比主成分分析（PCA）、t-SNE、线性判别分析（LDA）和变分自编码器（VAE）基线方法，本方法在对比度上最高提升20.1%，分类准确率最高提升17.4%，可解释性最高提升120.0%，同时将VAE重构性能提升9.5%。结果表明，基于统计依赖与独立性准则的可解释表示学习具有广阔前景。
原文摘要 · Abstract (English)
Conventional dimensionality reduction methods mainly optimize variance or correlation, leaving statistical dependence, data diversity, contrast, and interpretability under addressed. We propose three new independence criteria for designing supervised and unsupervised dimensionality reduction (DR) methods, aiming to improve feature extraction and representation quality. Our framework combines linear and nonlinear formulations and is evaluated using contrast, classification accuracy, and interpretability measures. The interpretability of eigenfaces helps to effectively summarize dominant class-specific structures and trends within representative images. Evaluated on MNIST and a Gender face dataset for classification and reconstruction, our methods achieve significant improvements in contrast (up to $+$20.1\%), accuracy (up to $+$17.4\%), and interpretability (up to $+$120.0\%) over Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Linear Discriminant Analysis (LDA), and Variational Autoencoder (VAE) baselines, while also improving VAE reconstruction performance by 9.5\%. These results suggest a promising direction for interpretable representation learning based on statistical dependence and independence criteria.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。