One-Class Support Tensor Machine

Chen, Yanyan; Wang, Kuaini; Zhong, Ping

doi:10.1016/j.knosys.2016.01.007

Cited by 37 publications

(20 citation statements)

References 26 publications

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“…• The optimization error measures, by closely we can compute the function that best satisfies the given information in our finite training set [12].…”

Section: Three Components Of the Generalization Errormentioning

confidence: 99%

SVM approach for non-parametric method in classification and regression learning process on feature selection with $epsilon$-insensitive region

Premalatha¹,

Vijayalakshmi²

2019

Malaya J. Mat

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Machine Learning is considered as a subfield of Artificial Intelligence. Machine Learning is concerned with the development of techniques and design the methods which enable the computer to learn. The field of machine learning is concerned with constructing computer program that automatically improve its performance with experience. In today's machine learning applications, support vector machines are considered (SVM) one of the most robust and accurate methods among all well-known algorithms and also being developed at a fast space. The aim of SVM is to find the best classification function, in a two-class learning task, and to distinguish between members of the two classes in the training data. Hence, the goal of machine learning is to find the output hypothesis that performed the correct classification of the training data, but the other earlier algorithms to find the hypothesis that accurate fit to the data. SVM requires that each data instance is represented as a vector of real numbers. Hence, if there are categorical attributes, convert them into numerical data, then we using m numbers to represent an m-category attribute. Only one of the m numbers is one, and others are zero. Machine learning has been applied in various field such as medical diagnosis, bioinformatics, detecting credit card fraud, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, and robot locomotion. The objective is algorithmic approach for non parametric methods to tractable for high dimensional massive datasets.

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“…• The optimization error measures, by closely we can compute the function that best satisfies the given information in our finite training set [12].…”

Section: Three Components Of the Generalization Errormentioning

confidence: 99%

SVM approach for non-parametric method in classification and regression learning process on feature selection with $epsilon$-insensitive region

Premalatha¹,

Vijayalakshmi²

2019

Malaya J. Mat

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“…The Anomaly Detection is a problem of finding instances of the input data that do not conform to the general pattern or behavior exhibited by the majority of the data points. This problem has been well explored and addressed in the past using One-class Classification [1,2,3,4,5,6,7,8]. The One-class Classification (OCC) problem is unlike the conventional binary and multi-class classification problems in that in OCC, one has data about only one of the many classes that could constitute the input space.…”

Section: Introductionmentioning

confidence: 99%

Localized Multiple Kernel learning for Anomaly Detection: One-class Classification

Gautam

Balaji

Sudharsan

et al. 2019

Knowledge-Based Systems

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Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic multi-kernel approach has been proposed to solve the OCC problem, which is simply a convex combination of different kernels with equal weights. This paper proposes a Localized Multiple Kernel learning approach for Anomaly Detection (LMKAD) using OCC, where the weight for each kernel is assigned locally. Proposed LMKAD approach adapts the weight for each kernel using a gating function. The parameters of the gating function and one-class classifier are optimized simultaneously through a two-step optimization process. We present the empirical results of the performance of LMKAD on 25 benchmark datasets from various disciplines. This performance is evaluated against existing Multi Kernel Anomaly Detection (MKAD) algorithm, and four other existing kernel-based one-class classifiers to showcase the credibility of our approach. LMKAD achieves significantly better Gmean scores while using a lesser number of support vectors compared to MKAD. Friedman test is also performed to verify the statistical significance of the results claimed in this paper.

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“…As a result, the tensor representation of data has become a new research direction in the field of data mining and machine learning [1][2][3][4][5]. Recent years has seen a growing attention being paid to tensor representation and its application in fields like image classification, face recognition, scene classification and bioinformatics [6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Tensor-Based Machine Learning Algorithm for Image Classification

Wang¹,

Chen²

2019

RIA

Self Cite

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In recent year, the tensor theory has been frequently incorporated to machine learning, because of the various advantages of tensor-based machine learning over vector-based machining learning: the ability to preserve the spatiotemporal information, allowing full utilization of the data, and the suitability for solving high-dimensional problems with a small sample size. Considering the suitability of tensor algorithm for classical high-dimensional, small-sample problems, this paper probes into the nonlinear classification problem with tensor representation, and designs a tensor-based nonlinear classification algorithm, namely, the kernel-based STM (KSTM). The maximum margin principle was adopted for the classification by the KSTM: the two types of samples are separated by the decision hyperplane as far as possible in the tensor space. Through numerical experiments, it is proved that the KSTM achieved better classification accuracy than the linear method, especially for the highdimensional problem with a small sample size.

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One-Class Support Tensor Machine

Cited by 37 publications

References 26 publications

SVM approach for non-parametric method in classification and regression learning process on feature selection with $epsilon$-insensitive region

SVM approach for non-parametric method in classification and regression learning process on feature selection with $epsilon$-insensitive region

Localized Multiple Kernel learning for Anomaly Detection: One-class Classification

A Nonlinear Tensor-Based Machine Learning Algorithm for Image Classification

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