# Population-Guided Large Margin Classifier for High-Dimension Low -Sample-Size Problems

arXiv: https://arxiv.org/abs/1901.01377 · HTML: https://mlsanity.com/paper/1901.01377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Random Forest Kernel for High-Dimension Low Sample Size Classification](https://mlsanity.com/paper/2310.14710/) (2023)
- [Robust Classification of High-Dimensional Data using Data-Adaptive Energy Distance](https://mlsanity.com/paper/2306.13985/) (2023)

## Abstract (arXiv)

Various applications in different fields, such as gene expression analysis or computer vision, suffer from data sets with high-dimensional low-sample-size (HDLSS), which has posed significant challenges for standard statistical and modern machine learning methods. In this paper, we propose a novel linear binary classifier, denoted by population-guided large margin classifier (PGLMC), which is applicable to any sorts of data, including HDLSS. PGLMC is conceived with a projecting direction w given by the comprehensive consideration of local structural information of the hyperplane and the statistics of the training samples. Our proposed model has several advantages compared to those widely used approaches. First, it is not sensitive to the intercept term b. Second, it operates well with imbalanced data. Third, it is relatively simple to be implemented based on Quadratic Programming. Fourth, it is robust to the model specification for various real applications. The theoretical properties of PGLMC are proven. We conduct a series of evaluations on two simulated and six real-world benchmark data sets, including DNA classification, digit recognition, medical image analysis, and face recognition. PGLMC outperforms the state-of-the-art classification methods in most cases, or at least obtains comparable results.