用稀疏建模从高维数据中提取可解释特征，提升木材表面缺陷检测效果
Sparse Modelling for Feature Learning in High Dimensional Data
- 结合Lasso与近端梯度法，构建可解释的特征选择流程
- 在木材缺陷检测任务中实现高准确率与高F1值
- 适合需要可解释性的工业质检场景
本文提出一种针对高维数据的维度缩减与特征提取新方法，重点应用于木材表面缺陷检测。框架整合稀疏建模技术（如Lasso和近端梯度法），构建高效且可解释的特征选择流程。通过引入预训练模型VGG19，并结合异常检测方法Isolation Forest与Local Outlier Factor，有效从复杂数据中提取有意义特征。采用准确率与F1分数等评估指标，辅以可视化分析，验证了稀疏建模方法在实际应用中的有效性。本研究旨在推动稀疏建模在机器学习中的理解与应用，尤其在木材表面缺陷检测领域。
原文摘要 · Abstract (English)
This paper presents an innovative approach to dimensionality reduction and feature extraction in high-dimensional datasets, with a specific application focus on wood surface defect detection. The proposed framework integrates sparse modeling techniques, particularly Lasso and proximal gradient methods, into a comprehensive pipeline for efficient and interpretable feature selection. Leveraging pre-trained models such as VGG19 and incorporating anomaly detection methods like Isolation Forest and Local Outlier Factor, our methodology addresses the challenge of extracting meaningful features from complex datasets. Evaluation metrics such as accuracy and F1 score, alongside visualizations, are employed to assess the performance of the sparse modeling techniques. Through this work, we aim to advance the understanding and application of sparse modeling in machine learning, particularly in the context of wood surface defect detection.
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