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Outliers' impact on high-dimension SVMs analyzed in new paper

By PulseAugur Editorial · [1 sources] ·

This paper investigates the impact of outliers on hard-margin linear Support Vector Machines (SVMs) in high-dimensional, low-sample-size scenarios. The research demonstrates that for one-sided contamination, the centered Gram matrix converges to a specific structure, allowing for a closed-form solution to the dual problem. This analysis provides explicit thresholds for issues like loss of inlier support and margin shrinkage. The study also examines contamination in both classes, revealing that symmetric contamination does not alter the limiting discriminant function despite margin reduction. AI

IMPACT Provides theoretical insights into the robustness of SVMs, potentially informing future algorithm development.

RANK_REASON Academic paper on a theoretical aspect of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Outliers' impact on high-dimension SVMs analyzed in new paper

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Academic paper on a theoretical aspect of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yugo Nakayama ·

    Outliers and the Hard-Margin Linear SVM in High-Dimension, Low-Sample-Size Settings

    arXiv:2611.13869v1 Announce Type: cross Abstract: We study how outliers affect the hard-margin linear support vector machine when the dimension grows while the sample sizes remain fixed. For one-sided contamination, the centered Gram matrix converges to a constant inlier block pl…

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