RECORD
Population-Guided Large Margin Classifier for High-Dimension Low -Sample-Size Problems
Machine Learning · Submitted 5 Jan 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim PGLMC outperforms the state-of-the-art classification methods in most cases, or at least obtains comparable results.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | 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. | contribution 0.97 | 0.90 | 0.12 | asserted 1.00 |
| s2 | 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. | method 0.99 | 0.78 | 0.04 | asserted 1.00 |
| s3 | Our proposed model has several advantages compared to those widely used approaches. | contribution 0.88 | 0.43 | 0.06 | asserted 0.99 |
| s4 | First, it is not sensitive to the intercept term b. | contribution 0.76 | 0.74 | 0.03 | asserted 1.00 |
| s5 | Second, it operates well with imbalanced data. | contribution 0.76 | 0.74 | 0.04 | asserted 1.00 |
| s6 | Third, it is relatively simple to be implemented based on Quadratic Programming. | contribution 0.83 | 0.57 | 0.03 | asserted 0.99 |
| s7 | Fourth, it is robust to the model specification for various real applications. | contribution 0.90 | 0.62 | 0.04 | asserted 1.00 |
| s8 | The theoretical properties of PGLMC are proven. | contribution 0.88 | 0.91 | 0.04 | asserted 1.00 |
| s9 | 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. | method 0.79 | 0.97 | 0.06 | asserted 0.99 |
| s10 | PGLMC outperforms the state-of-the-art classification methods in most cases, or at least obtains comparable results. | result 1.00 | 0.94 | 0.79 | asserted 0.96 |
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The paper
Original abstract
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.
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