Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures

Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture model-based approach for proportional vector clustering is proposed. In particular, a sophisticated entropy-based variational algorithm is developed to learn the model and optimize its complexity simultaneously. Moreover, a component-splitting principle is investigated, here, to handle the problem of model selection and to prevent over-fitting, which is an added advantage, as it is done within the variational framework. The performance and merits of the proposed framework are evaluated on multiple, real-challenging applications including dynamic textures clustering, objects categorization and human gesture recognition.

Few-Shot Abnormal Network Traffic Detection Based on Multi-scale Deep-CapsNet and Adversarial Reconstruction

Detecting various attacks and abnormal traffic in the network is extremely important to network security. Existing detection models used massive amounts of data to complete abnormal traffic detection. However, few-shot attack samples can only be intercepted in certain special scenarios. In addition, the discrimination of traffic attributes will also be affected by the change of feature attitude. But the traditional neural network model cannot detect this kind of attitude change. Therefore, the accuracy and efficiency of few-shot sample abnormal traffic detection are very low. In this paper, we proposed a few-shot abnormal network traffic detection method. It was composed of the multi-scale Deep-CapsNet and adversarial reconstruction. First, we designed an improved EM vector clustering of the Deep-CapsNet. The attitude transformation matrix was used to complete the prediction from low-level to high-level features. Second, a multi-scale convolutional capsule was designed to optimize the Deep-CapsNet. Third, an adversarial reconstruction classification network (ARCN) was proposed. The supervised source data classification and the unsupervised target data reconstruction were achieved. Moreover, we proposed an adversarial training strategy, which alleviated the noise interference during reconstruction. Fourth, the few-shot sample classification were obtained by combining multi-scale Deep-CapsNet and adversarial reconstruction. The ICSX2012 and CICIDS2017 datasets were used to verify the performance. The experimental results show that our method has better training performance. Moreover, it has the highest accuracy in two-classification and multi-classification. Especially it has good anti-noise performance and short running time, which can be used for real-time few-shot abnormal network traffic detection.

Minimum Distribution Support Vector Clustering

Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new clustering method: minimum distribution for support vector clustering (MDSVC), for improving the robustness of boundary point recognition, which characterizes the optimal hypersphere by the first-order and second-order statistics and tries to minimize the mean and variance simultaneously. In addition, we further prove, theoretically, that our algorithm can obtain better generalization performance. Some instructive insights for adjusting the number of support vector points are gained. For the optimization problem of MDSVC, we propose a double coordinate descent algorithm for small and medium samples. The experimental results on both artificial and real datasets indicate that our MDSVC has a significant improvement in generalization performance compared to SVC.

HYPERSPECTRAL IMAGE BAND SELECTION BASED ON SUBSPACE CLUSTERING

Aiming at the problems in hyperspectral image classification, such as high dimension, small sample and large computation time, this paper proposes a band selection method based on subspace clustering, and applies it to hyperspectral image land cover classification. This method considers each band image as a feature vector, clustering band images using subspace clustering method. After that, a representative band is selected from each cluster. Finally feature vector is formed on behalf of the representative bands, which completes the dimension reduction of hyperspectral data. SVM classifier is used to classify the new generated sample points. Experimental data show that compared with other methods, the new method effectively improves the accuracy of land cover recognition.

Pinball Loss Twin Support Vector Clustering

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.