Sparse Modelling for Feature Learning in High Dimensional Data
This work addresses wood surface defect detection, an incremental application of existing sparse modeling methods to a specific domain.
The paper tackled feature extraction for wood surface defect detection in high-dimensional data by integrating sparse modeling techniques like Lasso with pre-trained models and anomaly detection methods, achieving results evaluated with accuracy and F1 scores.
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.