Summary of Optimizing Sparse Generalized Singular Vectors For Feature Selection in Proximal Support Vector Machines with Application to Breast and Ovarian Cancer Detection, by Ugochukwu O. Ugwu and Michael Kirby
Optimizing Sparse Generalized Singular Vectors for Feature Selection in Proximal Support Vector Machines with Application to Breast and Ovarian Cancer Detection
by Ugochukwu O. Ugwu, Michael Kirby
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); Optimization and Control (math.OC); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)
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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 In this paper, researchers propose novel approaches for computing sparse solutions of Generalized Singular Value Problems (GSVPs). The GSVP is regularized by combining the _1-norm and _q-penalty, yielding the _1-GSVP and _q-GSVP formulations. To find these solutions, the authors employ the proximal gradient descent algorithm with a fixed step size. The resulting sparse solutions are then used for feature selection, which is integrated with non-parallel Support Vector Machines (SVMs) for binary classification. Specifically, the authors develop the _1-GSVPSVM and _q-GSVPSVM variants by combining SVM with the _1-GSVP and _q-GSVP frameworks. The proposed methods are applied to machine learning applications in cancer detection, achieving near-perfect balanced accuracy on breast and ovarian cancer datasets using only a few selected features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the right answers by solving complex math problems. Scientists use special formulas to make computers solve big math problems more efficiently. They take existing solutions and make them better by adding new parts that help the computer pick the most important information. This makes it easier for doctors to diagnose cancer accurately using only a few clues. |
Keywords
» Artificial intelligence » Classification » Feature selection » Gradient descent » Machine learning
