Recently, domain adaptation (DA) methods based on contrastive learning are widely used to solve the cross-scene classification problem. However, existing contrastive learning methods only focus on source domain or target domain features, or do not adequately consider the interaction of domain information, thus the learned domain-invariant features still have large discrepancies. To address this problem, we propose a novel domain fusion contrastive learning (DFCL) framework for cross-scene hyperspectral image (HSI) classification. DFCL uses an interdomain and intradomain dual-domain fusion strategy at the feature level, which introduces domain information as a noise interference term for sample enhancement. With the interference of domain information, same category samples are pulled closer and different categories samples are pushed further apart to learn more discriminative features. In addition, we construct an intermediate domain through the source and target domains and define a feature space loss that measures domain discrepancy by feature similarity and label similarity. Finally, a progressive selection strategy based on prototype learning is proposed to select high-confidence pseudolabels for DFCL. Experiments on three HSI cross-scene datasets show that the proposed method is superior to existing DA methods.

The divide-and-conquer strategy is a very effective method of dealing with big data. Noisy samples in big data usually have a great impact on algorithmic performance. In this article, we introduce Markov sampling and different weights for distributed learning with the classical support vector machine (cSVM). We first estimate the generalization error of weighted distributed cSVM algorithm with uniformly ergodic Markov chain (u.e.M.c.) samples and obtain its optimal convergence rate. As applications, we obtain the generalization bounds of weighted distributed cSVM with strong mixing observations and independent and identically distributed (i.i.d.) samples, respectively. We also propose a novel weighted distributed cSVM based on Markov sampling (DM-cSVM). The numerical studies of benchmark datasets show that the DM-cSVM algorithm not only has better performance but also has less total time of sampling and training compared to other distributed algorithms.

Convolutional neural network (CNN) has achieved great success in hyperspectral image (HSI) classification. However, the local receptive field of CNN leads to the drawback in extracting long-distance features. Transformer has excellent global modeling ability and shows good performance for HSI classification. The existing Transformer-based methods usually ignore a problem that the spatial information varies under different channels. To well describe the cross-channel dependencies, a cross-channel dynamic spatial-spectral fusion transformer (CDSFT) is proposed in this article. In the proposed CDSFT, the multi-scale and multi-channel features are extracted and then cross-channel global features are extracted through transpose multi-head self-attention (TMHSA). Next, a dynamic feature enhancement module and a spectral spatial position attention module are designed to extract and enhance spectral-spatial joint features for classification. Experimental results on three well-known HSI datasets demonstrate the effectiveness of the proposed CDSFT method.

In this article we consider Incremental Fisher linear discriminant (IFLD) based on data denoising. The data denoising is completed by Markov sampling such that the generated non-noise sample sequence is an uniformly ergodic Markov chain (u.e.M.c.). We first establish the generalization bounds of IFLD with u.e.M.c. samples, and prove that the IFLD algorithm with u.e.M.c. samples is consistent. We also present two new IFLD classification algorithms based on Markov sampling, IFLD based on Markov sampling (IFLD-MS) and improved IFLD based on Markov sampling (IIFLD-MS). Experimental results of benchmark repository suggest that IFLD-MS and IIFLD-MS have better performance than the classical IFLD, the incremental support vector machine (ISVM) and other IFLD algorithms.

Incremental learning is one of the most effective methods of learning accumulated data and large-scale data. The newly increased samples of the previously known works on incremental learning are usually independent and identically distributed. To study how dependent sampling methods influence the learning ability of incremental support vector machines (ISVM) algorithm, in this paper we introduce an ISVM based on Markov resampling (MR-ISVM), and give the experimental research on the learning ability of the MR-ISVM algorithm. The experimental results indicate that the MR-ISVM algorithm has not only smaller misclassification rates and sparser of the obtained classifiers, but also less total time of sampling and training compared to ISVM based on randomly independent sampling. We also compare it with other ISVM algorithms.

Support vector machine (SVM) is one of the most widely used learning algorithms for classification problems. Although SVM has good performance in practical applications, it has high algorithmic complexity as the size of training samples is large. In this paper, we introduce SVM classification (SVMC) algorithm based on -times Markov sampling and present the numerical studies on the learning performance of SVMC with -times Markov sampling for benchmark data sets. The experimental results show that the SVMC algorithm with -times Markov sampling not only have smaller misclassification rates, less time of sampling and training, but also the obtained classifier is more sparse compared with the classical SVMC and the previously known SVMC algorithm based on Markov sampling. We also give some discussions on the performance of SVMC with -times Markov sampling for the case of unbalanced training samples and large-scale training samples.

UNLABELLED: The previously known works studying the generalization ability of support vector machine classification (SVMC) algorithm are usually based on the assumption of independent and identically distributed samples. In this paper, we go far beyond this classical framework by studying the generalization ability of SVMC based on uniformly ergodic Markov chain (u.e.M.c.) samples. We analyze the excess misclassification error of SVMC based on u.e.M.c. samples, and obtain the optimal learning rate of SVMC for u.e.M.c. SAMPLES: We also introduce a new Markov sampling algorithm for SVMC to generate u.e.M.c. samples from given dataset, and present the numerical studies on the learning performance of SVMC based on Markov sampling for benchmark datasets. The numerical studies show that the SVMC based on Markov sampling not only has better generalization ability as the number of training samples are bigger, but also the classifiers based on Markov sampling are sparsity when the size of dataset is bigger with regard to the input dimension.

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

This paper considers the generalization ability of two regularized regression algorithms [least square regularized regression (LSRR) and support vector machine regression (SVMR)] based on non-independent and identically distributed (non-i.i.d.) samples. Different from the previously known works for non-i.i.d. samples, in this paper, we research the generalization bounds of two regularized regression algorithms based on uniformly ergodic Markov chain (u.e.M.c.) samples. Inspired by the idea from Markov chain Monto Carlo (MCMC) methods, we also introduce a new Markov sampling algorithm for regression to generate u.e.M.c. samples from a given dataset, and then, we present the numerical studies on the learning performance of LSRR and SVMR based on Markov sampling, respectively. The experimental results show that LSRR and SVMR based on Markov sampling can present obviously smaller mean square errors and smaller variances compared to random sampling.

Improving the availability of services of is a key issue for survivability of a cluster system. Lots of schemes are proposed for this purpose. But most of them aim at enhancing only the service-level availability or application specific. In this paper, we propose a scheme called High Availability with Redundant TCP Stacks (HARTs), providing connection-level availability by exploring the redundant TCP stacks for TCP connections at the server side. We present our performance experiment results on our HA cluster prototype. From results, we find the configuration of one primary server with one backup server running on separated 100 Mbps Ethernet has acceptable performance to support the server side applications while delivering high availability.