An Improved Moving Object Detection Algorithm Based on Gaussian Mixture Models

Aiming at the problems that the classical Gaussian mixture model is unable to detect the complete moving object, and is sensitive to the light mutation scenes and so on, an improved algorithm is proposed for moving object detection based on Gaussian mixture model and three-frame difference method. In the process of extracting the moving region, the improved three-frame difference method uses the dynamic segmentation threshold and edge detection technology, and it is first used to solve the problems such as the illumination mutation and the discontinuity of the target edge. Then, a new adaptive selection strategy of the number of Gaussian distributions is introduced to reduce the processing time and improve accuracy of detection. Finally, HSV color space is used to remove shadow regions, and the whole moving object is detected. Experimental results show that the proposed algorithm can detect moving objects in various situations effectively.

A Variational Model for Removing Multiple Multiplicative Noises

The problem of multiplicative noise removal has been widely studied in recent years. Many methods have been used to remove it, but the final results are not very excellent. The total variation regularization method to solve the problem of the noise removal can preserve edge well, but sometimes produces undesirable staircasing effect. In this paper, we propose a variational model to remove multiplicative noise. An alternative algorithm is employed to solve variational model minimization problem. Experimental results show that the proposed model can not only effectively remove Gamma noise, but also Rayleigh noise, as well as the staircasing effect is significantly reduced.

Feature Selection: Algorithms and Challenges

Feature selection is an active area in data mining research and development. It consists of efforts and contributions from a wide variety of communities, including statistics, machine learning, and pattern recognition. The diversity, on one hand, equips us with many methods and tools. On the other hand, the profusion of options causes confusion.This paper reviews various feature selection methods and identifies research challenges that are at the forefront of this exciting area.

Multi-view Feature Learning for the Over-penalty in Adversarial Domain Adaptation

Domain adaptation aims to transfer knowledge from the labeled source domain to an unlabeled target domain that follows a similar but different distribution.Recently,adversarial-based methods have achieved remarkable success due to the excellent performance of domain-invariant feature presentation learning.However,the adversarial methods learn the transferability at the expense of the discriminability in feature representation,leading to low generalization to the target domain.To this end,we propose a Multi-view Feature Learning method for the Over-penalty in Adversarial Domain Adaptation.Specifically,multi-view representation learning is proposed to enrich the discriminative information contained in domain-invariant feature representation,which will counter the over-penalty for discriminability in adversarial training.Besides,the class distribution in the intra-domain is proposed to replace that in the inter-domain to capture more discriminative information in the learning of transferrable features.Extensive experiments show that our method can improve the discriminability while maintaining transferability and exceeds the most advanced methods in the domain adaptation benchmark datasets.

基于图神经网络的环状RNA生物标志物筛选预测算法

越来越多的证据表明,环状RNA(circular RNA,circRNA)在人类复杂疾病发病机制和许多重要生物学过程中发挥不可或缺的作用.确定环状RNA与疾病之间关联对于复杂人类疾病的诊断和治疗具有重要的潜在价值.然而,传统的湿实验方式通常是盲目、低效、耗时且昂贵的,往往还伴随着高的假阳性率.因此,迫切需要有效和可行的计算方法来大规模预测潜在的环状RNA–疾病关联.本文通过结合图神经网络的高阶图卷积网络算法与随机蕨分类器对环状RNA与疾病之间的关联关系进行预测.该方法能够从环状RNA和疾病多种属性信息构建的多源相似性网络中,有效抽取具有高阶混合邻域信息的高级特征,并对其进行准确分类.在5折交叉验证实验中,该方法在CircR2Disease数据集上取得了93.75%的AUC得分.此外,在案例研究中,该模型的预测结果得到了生物湿实验的支持,预测得分前15的环状RNA–疾病关联中的13个在最近发表文献中得以证实.这些优异的结果表明,所提模型是预测环状RNA–疾病关联的有效工具,并且可以为生物湿实验提供理论依据和高可信的环状RNA候选生物标志物.

A survey on online feature selection with streaming features

In the era of big data, the dimensionality of data is increasing dramatically in many domains. To deal with high dimensionality, online feature selection becomes critical in big data mining. Recently, online selection of dynamic features has received much attention. In situations where features arrive sequentially over time, we need to perform online feature selection upon feature arrivals. Meanwhile, considering grouped features, it is necessary to deal with features arriving by groups. To handle these challenges, some state-of- the-art methods for online feature selection have been proposed. In this paper, we first give a brief review of traditional feature selection approaches. Then we discuss specific problems of online feature selection with feature streams in detail. A comprehensive review of existing online feature selection methods is presented by comparing with each other. Finally, we discuss several open issues in online feature selection.