Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Xue, Nan; Luo, Xiong; Gao, Yang; Wang, Weiping; Wang, Long; Huang, Chao; Zhao, Wenbing

doi:10.3390/e21080785

Cited by 6 publications

(4 citation statements)

References 43 publications

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“…Adaptive kernel filters are widely used in time-series prediction [ 46 ]. We tested the devised algorithm to model a Lorentz chaotic system [ 30 ]: where α = 8/3, δ = 10, and θ = 28 [ 11 ].…”

Section: Numerical Experimentsmentioning

confidence: 99%

Anisotropic Gaussian kernel adaptive filtering by Lie-group dictionary learning

et al. 2020

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The present paper proposes a novel kernel adaptive filtering algorithm, where each Gaussian kernel is parameterized by a center vector and a symmetric positive definite (SPD) precision matrix, which is regarded as a generalization of scalar width parameter. In fact, different from conventional kernel adaptive systems, the proposed filter is structured as a superposition of non-isotropic Gaussian kernels, whose non-isotropy makes the filter more flexible. The adaptation algorithm will search for optimal parameters in a wider parameter space. This generalization brings the need of special treatment of parameters that have a geometric structure. In fact, the main contribution of this paper is to establish update rules for precision matrices on the Lie group of SPD matrices in order to ensure their symmetry and positive-definiteness. The parameters of this filter are adapted on the basis of a leastsquares criterion to minimize the filtering error, together with an ℓ 1-type regularization criterion to avoid overfitting and to prevent the increase of dimensionality of the dictionary. Experimental results confirm the validity of the proposed method.

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Section: Numerical Experimentsmentioning

confidence: 99%

Anisotropic Gaussian kernel adaptive filtering by Lie-group dictionary learning

et al. 2020

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“…In our previous works, to measure the stability of cyber-physical systems (CPSs) under malicious attacks, we developed a finitetime observer to estimate the state of the CPSs [6]. Then, we proposed a kernel learning algorithm to improve the malware detection performance on complex datasets with noise [7]. In addition to detection performance, memory footprint and response speed are also of enormous importance for current smart devices on IoT, and this poses higher requirements for edge malware analysis.…”

Section: Introductionmentioning

confidence: 99%

Categorizing Malware via A Word2Vec-based Temporal Convolutional Network Scheme

et al. 2020

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As edge computing paradigm achieves great popularity in recent years, there remain some technical challenges that must be addressed to guarantee smart device security in Internet of Things (IoT) environment. Generally, smart devices transmit individual data across the IoT for various purposes nowadays, and it will cause losses and impose a huge threat to users since malware may steal and damage these data. To improve malware detection performance on IoT smart devices, we conduct a malware categorization analysis based on the Kaggle competition of Microsoft Malware Classification Challenge (BIG 2015) dataset in this article. Practically speaking, motivated by temporal convolutional network (TCN) structure, we propose a malware categorization scheme mainly using Word2Vec pre-trained model. Considering that the popular one-hot encoding converts input names from malicious files to high-dimensional vectors since each name is represented as one dimension in one-hot vector space, more compact vectors with fewer dimensions are obtained through the use of Word2Vec pre-training strategy, and then it can lead to fewer parameters and stronger malware feature representation. Moreover, compared with long short-term memory (LSTM), TCN demonstrates better performance with longer effective memory and faster training speed in sequence modeling tasks. The experimental comparisons on this malware dataset reveal better categorization performance with less memory usage and training time. Especially, through the performance comparison between our scheme and the state-of-the-art Word2Vec-based LSTM approach, our scheme shows approximately 1.3% higher predicted accuracy than the latter on this malware categorization task. Additionally, it also demonstrates that our scheme reduces about 90 thousand parameters and more than 1 hour on the model training time in this comparison.

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“…The classification of data is an important research topic in the field of data mining [1][2][3][4][5][6]. Intrusion detection, weather forecasting, face recognition, product recommendations, and so forth are some important applications of classification.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data

Azhar

Huang

2019

Entropy

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Data classification is an important research topic in the field of data mining. With the rapid development in social media sites and IoT devices, data have grown tremendously in volume and complexity, which has resulted in a lot of large and complex high-dimensional data. Classifying such high-dimensional complex data with a large number of classes has been a great challenge for current state-of-the-art methods. This paper presents a novel, hierarchical, gamma mixture model-based unsupervised method for classifying high-dimensional data with a large number of classes. In this method, we first partition the features of the dataset into feature strata by using k-means. Then, a set of subspace data sets is generated from the feature strata by using the stratified subspace sampling method. After that, the GMM Tree algorithm is used to identify the number of clusters and initial clusters in each subspace dataset and passing these initial cluster centers to k-means to generate base subspace clustering results. Then, the subspace clustering result is integrated into an object cluster association (OCA) matrix by using the link-based method. The ensemble clustering result is generated from the OCA matrix by the k-means algorithm with the number of clusters identified by the GMM Tree algorithm. After producing the ensemble clustering result, the dominant class label is assigned to each cluster after computing the purity. A classification is made on the object by computing the distance between the new object and the center of each cluster in the classifier, and the class label of the cluster is assigned to the new object which has the shortest distance. A series of experiments were conducted on twelve synthetic and eight real-world data sets, with different numbers of classes, features, and objects. The experimental results have shown that the new method outperforms other state-of-the-art techniques to classify data in most of the data sets.

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Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Cited by 6 publications

References 43 publications

Anisotropic Gaussian kernel adaptive filtering by Lie-group dictionary learning

Anisotropic Gaussian kernel adaptive filtering by Lie-group dictionary learning

Categorizing Malware via A Word2Vec-based Temporal Convolutional Network Scheme

A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data

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