Summary of Sparse Modelling For Feature Learning in High Dimensional Data, by Harish Neelam et al.
Sparse Modelling for Feature Learning in High Dimensional Data
by Harish Neelam, Koushik Sai Veerella, Souradip Biswas
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework integrates sparse modeling techniques into a comprehensive pipeline for efficient and interpretable feature selection, specifically focusing on high-dimensional datasets with a specific application in wood surface defect detection. The approach combines pre-trained models like VGG19 with anomaly detection methods like Isolation Forest and Local Outlier Factor to extract meaningful features from complex datasets. Evaluation metrics such as accuracy and F1 score are used alongside visualizations to assess the performance of the sparse modeling techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to reduce the number of features in big datasets, with a special focus on finding defects on wood surfaces. The method uses “sparse” models that can remove unnecessary data points while keeping important ones. It also combines this approach with other methods for detecting unusual patterns. The results show that this method works well and is easy to understand. |
Keywords
» Artificial intelligence » Anomaly detection » F1 score » Feature selection
