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Classification for high-dimension low-sample size data

PATTERN RECOGNITION 2022年 130卷

作者： Shen, Liran Er, Meng Joo Yin, Qingbo Dalian Maritime Univ Coll Marine Elect Engn Dalian 116023 Peoples R China Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116023 Peoples R China

High-dimension and low-sample-size (HDLSS) data sets have posed great challenges to many machine learning methods. To deal with practical HDLSS problems, development of new classification techniques is highly desired. After the cause of the over-fitting phenomenon is identified, a new classification criterion for HDLSS data sets, termed tolerance similarity, is proposed to emphasize maximization of within-class variance on the premise of class separability. Leveraging on this criterion, a novel linear binary classifier, termed No-separated Data Maximum Dispersion classifier (NPDMD), is designed. The main idea of the NPDMD is to spread samples of two classes in a large interval in the respective positive or negative space along the projecting direction when the distance between the projection means for two classes is large enough. The salient features of the proposed NPDMD are: (1) The NPDMD operates well on HDLSS data sets;(2) The NPDMD solves the objective function in the entire feature space to avoid the data piling phenomenon. (3) The NPDMD leverages on the low-rank property of the covariance matrix for HDLSS data sets to accelerate the computation speed. (4) The NPDMD is suitable for different real-word applications. (5) The NPDMD can be implemented readily using Quadratic Programming. Not only theoretical properties of the NPDMD have been derived, but also a series of evaluations have been conducted on one simulated and six real-world benchmark data sets, including face classification and mRNA classification. Experimental results and comprehensive studies demonstrate the superiority of the NPDMD in terms of correct classification rate, mean within-group correct classification rate and the area under the ROC curve.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： binary linear classifier Quadratic programming Data piling Covariance matrix

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Population structure-learned classifier for high-dimension low-sample-size class-imbalanced problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期111卷

作者： Shen, Liran Er, Meng Joo Liu, Weijiang Fan, Yunsheng Yin, Qingbo Dalian Maritime Univ Coll Marine Elect Engn Dalian 116023 Peoples R China Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116023 Peoples R China

The classification on high-dimension low-sample-size data (HDLSS) is a challenging problem and it is common to have class-imbalanced data in most application fields. We term this as Imbalanced HDLSS (IHDLSS). Recent theoretical results reveal that the classification criterion and tolerance similarity are crucial to HDLSS, which emphasizes the maximization of within-class variance on the premise of class separability. Based on this idea, a novel linear binary classifier, termed Population Structure-learned classifier (PSC), is proposed. The proposed PSC can obtain better generalization performance on IHDLSS by maximizing the sum of inter-class scatter matrix and intra-class scatter matrix on the premise of class separability and assigning different intercept values to majority and minority classes. The salient features of the proposed approach are: (1) It works well on IHDLSS;(2) The inverse of high dimensional matrix can be solved in low dimensional space;(3) It is self-adaptive in determining the intercept term for each class;(4) Its computational complexity is analyzed. A series of evaluations are conducted on one simulated data set and ten real-world benchmark data sets on IHDLSS on gene analysis. Experimental results demonstrate that the PSC is superior to the state-of-art methods in IHDLSS.

关键词： High-dimension low-sample-size binary linear classifier Data piling Class-imbalanced data Population structure-learned

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Population-guided large margin classifier for high-dimension low-sample-size problems

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PATTERN RECOGNITION 2020年 97卷 107030-107030页

作者： Yin, Qingbo Adeli, Ehsan Shen, Liran Shen, Dinggang Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116023 Peoples R China Univ North Carolina Dept Radiol Chapel Hill NC 27599 USA Univ North Carolina BRIC Chapel Hill NC 27599 USA Stanford Univ Dept Psychiat & Behav Sci Stanford CA 94305 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA Dalian Maritime Univ Coll Marine Elect Engn Dalian 116023 Peoples R China Korea Univ Dept Brain & Cognit Engn Seoul 02841 South Korea

In this paper, we propose a novel linear binary classifier, denoted by population-guided large margin classifier (PGLMC), applicable to any sorts of data, including high-dimensional low-sample-size (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 isn't 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 the simulated and five realworld benchmark data sets, including DNA classification, medical image analysis and face recognition. PGLMC outperforms the state-of-the-art classification methods in most cases, or obtains comparable results. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： binary linear classifier Data piling High-dimension lowsample-size Hyperplane Large margin classification Local structure information

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Data maximum dispersion classifier in projection space for high-dimension low-sample-size problems

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KNOWLEDGE-BASED SYSTEMS 2020年 193卷 105420-105420页

作者： Shen, Liran Yin, Qingbo Dalian Maritime Univ Coll Marine Elect Engn Dalian 116023 Peoples R China Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116023 Peoples R China

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 Data Maximum Dispersion classifier (DMDC), which is applicable to general data, especially HDLSS such as genetic microarrays, image and so on. DMDC maximize data dispersion in projection space to find the hyperplane for classification. Our proposed model has several advantages compared to those widely used approaches. First, it works well on HDLSS. Second, it is not sensitive to the intercept term b. Third, the implement of DMDC is easy and holds low computational complexity owing to solve the similar Convex Quadratic Programming formulation as in SVM, rather than the SOCP in the DWD related methods. Fourth, it is robust to the model specification for various real applications. We conduct the evaluations on one simulated and seven real-world benchmark data sets, including face classification, radiomics and mRNA classification. The experiment results manifested the superiority of DMDC compared to the state-of-art algorithms in most cases, or at least obtains comparable results. (c) 2019 Elsevier B.V. All rights reserved.

关键词： High-dimension low-sample-size binary linear classifier Data piling Hyperplane Large margin classification

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Adaptive Multipattern Fast Block-Matching Algorithm Based on Motion Classification Techniques

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2008年第10期18卷 1369-1382页

作者： Gonzalez-Diaz, Ivan Diaz-de-Maria, Fernando Univ Carlos III Madrid Dept Signal Theory & Commun Madrid 28911 Spain

In most video coding standards, motion estimation becomes the most time-consuming subsystem. Consequently, in the last few years, a great deal of effort has been devoted to the research of novel algorithms capable of saving computations with minimal effects on the coding quality. Adaptive algorithms and particularly multipattern solutions, have evolved as the most robust general-purpose solutions owing to two main reasons: 1) real video sequences usually exhibit a wide-range of motion content, front uniform to random, and 2) a vast amount of coding applications have appeared demanding different degrees of coding quality. In this study. we propose an adaptive algorithm, called motion classification-based search (MCS), which makes use of an especially tailored classifier that detects some motion cues and chooses the search pattern that best fits them. The MCS has been experimentally assessed for a comprehensive set of selected video sequences and qualities. Our experimental results show that MCS notably reduces the computational cost up to 55 % and 84 % in search points, with respect to two state-of-the-art methods, while maintaining the quality.

关键词： binary linear classifier block-matching motion classification motion estimation multipattern algorithms

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Face recognition based on a novel linear discriminant criterion

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PATTERN ANALYSIS AND APPLICATIONS 2007年第3期10卷 165-174页

作者： Song, Fengxi Zhang, David Chen, Qinglong Wang, Jizhong Harbin Inst Technol Biocomp Res Ctr Shenzhen Grad Sch Sili 518055 Shenzhen Peoples R China New Star Res Inst Appl Tech Hefei 230031 Peoples R China Hong Kong Polytech Univ Kawloon Hong Kong Peoples R China

As an effective technique for feature extraction and pattern classification Fisher linear discriminant (FLD) has been successfully applied in many fields. However, for a task with very high-dimensional data such as face images, conventional FLD technique encounters a fundamental difficulty caused by singular within-class scatter matrix. To avoid the trouble, many improvements on the feature extraction aspect of FLD have been proposed. In contrast, studies on the pattern classification aspect of FLD are quiet few. In this paper, we will focus our attention on the possible improvement on the pattern classification aspect of FLD by presenting a novel linear discriminant criterion called maximum scatter difference (MSD). Theoretical analysis demonstrates that MSD criterion is a generalization of Fisher discriminant criterion, and is the asymptotic form of discriminant criterion: large margin linear projection. The performance of MSD classifier is tested in face recognition. Experiments performed on the ORL, Yale, FERET and AR databases show that MSD classifier can compete with top-performance linear classifiers such as linear support vector machines, and is better than or equivalent to combinations of well known facial feature extraction methods, such as eigenfaces, Fisherfaces, orthogonal complementary space, nullspace, direct linear discriminant analysis, and the nearest neighbor classifier.

关键词： Fisher linear discriminant small sample size problem pattern classification multi-objective programming binary linear classifier face recognition

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Adaptive multi-pattern fast block-matching algorithm based on motion classification techniques

Adaptive multi-pattern fast block-matching algorithm based o...

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32nd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Gonzalez-Diaz, Ivan de-Frutos-Lopez, Manuel Sanz-Rodriguez, Sergio Diaz-de-Maria, Fernando Univ Carlos III Madrid Dept Signal Theory & Commun Madrid Spain

ISBN: (纸本)9781424407286

Motion estimation is the most time-consuming subsystem in a video codec. Thus, more efficient methods of motion estimation should be investigated. Real video sequences usually exhibit a wide-range of motion content as well as different degrees of detail, which become particularly difficult to manage by typical block-matching algorithms. Recent developments in the area of motion estimation have focused on the adaptation to video contents. Adaptive thresholds and multi-pattern search algorithms have shown to achieve good performance when they success to adjust to motion characteristics. This paper proposes an adaptive algorithm, called MCS, that makes use of an especially tailored classifier that detects some motion cues and chooses the search pattern that best fits to them. Specifically, a hierarchical structure of binary linear classifiers is proposed. Our experimental results show that MCS notably reduces the computational cost with respect to an state-of-the-art method while maintaining the quality.

关键词： block-matching motion estimation motion classification binary linear classifier

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Improved motion classification techniques for adaptive multi-pattern fast block-matching algorithm

Improved motion classification techniques for adaptive multi...

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IEEE International Conference on Image Processing (ICIP 2007)

作者： Gonzalez-Diaz, Ivan Diaz-de-Maria, Fernando Univ Carlos III Madrid Dept Signal Theory & Commun Madrid Spain

ISBN: (纸本)9781424414369

In several video coding standards, such as H.264, motion estimation becomes the most-time consuming subsystem. Therefore, recently research on video coding focuses on the development of novel algorithms able to save computations with minimal effects over the coding distortion. Due to the fact that real video sequences usually exhibit a wide-range of motion content, from uniform to random, and to the vast amount of coding applications demanding different degrees of coding quality, adaptive algorithms have revealed as the most robust general purpose solutions. In particular, multi-pattern algorithms can adapt to video contents as well as to required coding quality by means of the use of a set of heterogeneus search patterns, each one adapting better to particular motion and quality requirements. This paper applies some improvements to the Motion Classification based Search, an adaptive multi-pattern algorithm based on motion classification techniques. Our experimental results show that MCS notably reduces the computational cost with respect to some well-known algorithms while maintaining the quality.

关键词： block-matching motion estimation multi-pattern algorithms motion classification binary linear classifier

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