An analysis of alignment and integral based kernels for machine learning from vessel trajectories

In this paper we present an analysis of the application of the two most important types of similarity measures for moving object trajectories in machine learning from vessel movement data. These similarities are applied in the tasks of clustering, classification and outlier detection. The first similarity type are alignment measures, such as dynamic time warping and edit distance. The second type are based on the integral over time between two trajectories. Following earlier work we define these measures in the context of kernel methods, which provide state-of-the-art, robust algorithms for the tasks studied. Furthermore, we include the influence of applying piecewise linear segmentation as pre-processing to the vessel trajectories when computing alignment measures, since this has been shown to give a positive effect in computation time and performance.In our experiments the alignment based measures show the best performance. Regular versions of edit distance give the best performance in clustering and classification, whereas the softmax variant of dynamic time warping works best in outlier detection. Moreover, piecewise linear segmentation has a positive effect on alignments, due to the fact that salient points in a trajectory, especially important in clustering and outlier detection, are highlighted by the segmentation and have a large influence in the alignments. Based on our experiments, integral over time based similarity measures are not well-suited for learning from vessel trajectories.     

一种面向高维混合属性数据的异常挖掘算法

异常检测是数据挖掘领域研究的最基本的问题之一,它在欺诈甄别、气象预报、客户分类和入侵检测等方面有广泛的应用。针对网络入侵检测的需求提出了一种新的基于混合属性聚类的异常挖掘算法,并且依据异常点(outliers)是数据集中的稀有点这一本质,给出了一种新的数据相似性和异常度的定义。本文所提出算法具有线性时间复杂度,在KDDCUP99和WisconsinPrognosisBreastCancer数据集上的实验表明,算本法在提供了近似线性时间复杂度和很好的可扩展性的同时,能够较好的发现数据集中的异常点。     

Dual geometric perception for cross-domain road segmentation

Road segmentation plays an important role in navigation systems and autonomous driving. However, many methods in road segmentation are based on supervised learning and suffer from performance degradation in the real world. There is a certain domain gap (distribution shift problem) between the source domain (training data) and the target domain (testing data). In this paper, we propose a Dual-Geometric Perception (DGP) approach for cross-domain road segmentation, which jointly uses semantic and dual-geometric information to learn the domain-invariant feature for road segmentation. First, we propose an RGB-N dual stream network structure, which effectively fuses normal vector information and RGB information to reduce domain gap. Moreover, a dual geometric adversarial learning strategy is proposed to utilize depth-aware and normal vector features to perform better domain alignment. Furthermore, a self-training learning strategy is used to further improve the model’s generalizability in the target domain. Extensive experiments demonstrate that our proposed DGP achieves superior performance on lane-to-lane and lane-to-sidewalk road domain adaptation tasks.     

基于最近邻相似度的孤立点检测及半监督聚类算法

传统的聚类算法是一种无监督的学习过程,聚类的精度受到相似性度量方式以及数据集中孤立点的影响,并且算法也没有很好的利用先验知识,无法体现用户的需求。因此提出了基于共享最近邻的孤立点检测及半监督聚类算法。该算法采用共享最近邻为相似度,根据数据点的最近邻居数目来判断是否为孤立点,并在删除孤立点的数据集上进行半监督聚类。在半监督聚类过程中加入了经过扩展的先验知识,同时根据图形分割原理对数据集进行聚类。文中使用真实的数据集进行仿真,其仿真结果表明,本文所提出的算法能有效的检测出孤立点,并具有很好的聚类效果。     

Skin segmentation using color pixel classification: analysis and comparison

This work presents a study of three important issues of the color pixel classification approach to skin segmentation: color representation, color quantization, and classification algorithm. Our analysis of several representative color spaces using the Bayesian classifier with the histogram technique shows that skin segmentation based on color pixel classification is largely unaffected by the choice of the color space. However, segmentation performance degrades when only chrominance channels are used in classification. Furthermore, we find that color quantization can be as low as 64 bins per channel, although higher histogram sizes give better segmentation performance. The Bayesian classifier with the histogram technique and the multilayer perceptron classifier are found to perform better compared to other tested classifiers, including three piecewise linear classifiers, three unimodal Gaussian classifiers, and a Gaussian mixture classifier.     

一种基于离群点信息的新型无监督聚类方法

在图像检索领域，聚类分析技术有着广泛应用。因为在对图像进行聚类分析时，通常缺少可资利用的先验知识，所以需要采用无监督的聚类算法。为了适应图像检索的需要，提出了一种新型的无监督聚类方法，即根据离群点信息来自动确定聚类算法的终止时机。此方法还弥补了现有聚类算法在离群点识别、使用上的缺欠。为验证其可行性，用其改进了CURE和ROCK两个经典算法。实验表明，改进后的两个算法都能自动终止，并能取得优于以往的聚类效果。     

Trajectory outlier detection approach based on common slices sub-sequence

Trajectory outlier detection is one of the most popular trajectory data mining topics. It helps researchers obtain a lot of valuable information that can be used as important guidance in monitoring and forecasting. Existing methods have difficulty in detecting the outlying trajectories with continuous multi-segment exception. To address the problem, in this paper, we propose a novel trajectory outlier detection algorithm based on common slices sub-sequence (TODCSS). For each trajectory, the direction-code sequence is firstly calculated based on the direction of each trajectory segment. Secondly, the corresponding sequence consisting of trajectory slices is obtained by inflection point segmentation. And then, the common slices sub-sequences between two trajectories are found to measure their distance. Finally, the slice outliers and trajectory outliers are detected based on the new CSS distance calculation. Both the intuitive visualization presentation and the experimental results on real Atlantic hurricane dataset, real-life mobility trajectory dataset of taxis in San Francisco and synthetic labeled dataset show that the proposed TODCSS algorithm effectively detects slice and trajectory outliers, and improves accuracy and stability in trajectory outlier detection.     

Locality kernels for sequential data and their applications to parse ranking

We propose a framework for constructing kernels that take advantage of local correlations in sequential data. The kernels designed using the proposed framework measure parse similarities locally, within a small window constructed around each matching feature. Furthermore, we propose to incorporate positional information inside the window and consider different ways to do this. We applied the kernels together with regularized least-squares (RLS) algorithm to the task of dependency parse ranking using the dataset containing parses obtained from a manually annotated biomedical corpus of 1100 sentences. Our experiments show that RLS with kernels incorporating positional information perform better than RLS with the baseline kernel functions. This performance gain is statistically significant. An erratum to this article can be found at     

一种电力感知数据的离群点检测方案

鉴于离群点引发的数据质量问题给电力应用造成的不良影响,对电力感知数据的特征进行了分析,并基于电力感知数据的时间特征和异常检测技术的易用性需求,提出一种电力感知数据的离群点检测方案。该方案由异常检测服务框架和离群点检测方法构成。异常检测服务框架借鉴Web服务的思想,基于大数据技术,能够支持电力感知数据的存储和计算,并且以服务的形式提供电力感知数据的异常检测能力。离群点检测方法是基于聚类算法和考虑时间属性的数据分段方法来检测电力感知数据中的离群点异常。通过实验验证了该方法的可行性和有效性,结果表明该方法能够有效识别具有时间相关性和连续性的电力感知数据中存在的离群点,且在数据规模增大时,具有良好的并行性和可扩展性。     

Cascaded model adaptation for dialog act segmentation and tagging

There are many speech and language processing problems which require cascaded classification tasks. While model adaptation has been shown to be useful in isolated speech and language processing tasks, it is not clear what constitutes system adaptation for such complex systems. This paper studies the following questions: In cases where a sequence of classification tasks is employed, how important is to adapt the earlier or latter systems? Is the performance improvement obtained in the earlier stages via adaptation carried on to later stages in cases where the later stages perform adaptation using similar data and/or methods? In this study, as part of a larger scale multiparty meeting understanding system, we analyze various methods for adapting dialog act segmentation and tagging models trained on conversational telephone speech (CTS) to meeting style conversations. We investigate the effect of using adapted and unadapted models for dialog act segmentation with those of tagging, showing the effect of model adaptation for cascaded classification tasks. Our results indicate that we can achieve significantly better dialog act segmentation and tagging by adapting the out-of-domain models, especially when the amount of in-domain data is limited. Experimental results show that it is more effective to adapt the models in the latter classification tasks, in our case dialog act tagging, when dealing with a sequence of cascaded classification tasks.     

Weighted pseudometric discriminatory power improvement using a Bayesian logistic regression model based on a variational method

In this paper, we investigate the effectiveness of a Bayesian logistic regression model to compute the weights of a pseudo-metric, in order to improve its discriminatory capacity and thereby increase image retrieval accuracy. In the proposed Bayesian model, the prior knowledge of the observations is incorporated and the posterior distribution is approximated by a tractable Gaussian form using variational transformation and Jensen's inequality, which allow a fast and straightforward computation of the weights. The pseudo-metric makes use of the compressed and quantized versions of wavelet decomposed feature vectors, and in our previous work, the weights were adjusted by classical logistic regression model. A comparative evaluation of the Bayesian and classical logistic regression models is performed for content-based image retrieval as well as for other classification tasks, in a decontextualized evaluation framework. In this same framework, we compare the Bayesian logistic regression model to some relevant state-of-the-art classification algorithms. Experimental results show that the Bayesian logistic regression model outperforms these linear classification algorithms, and is a significantly better tool than the classical logistic regression model to compute the pseudo-metric weights and improve retrieval and classification performance. Finally, we perform a comparison with results obtained by other retrieval methods.     

Efficient biased sampling for approximate clustering and outlier detection in large data sets

We investigate the use of biased sampling according to the density of the data set to speed up the operation of general data mining tasks, such as clustering and outlier detection in large multidimensional data sets. In density-biased sampling, the probability that a given point will be included in the sample depends on the local density of the data set. We propose a general technique for density-biased sampling that can factor in user requirements to sample for properties of interest and can be tuned for specific data mining tasks. This allows great flexibility and improved accuracy of the results over simple random sampling. We describe our approach in detail, we analytically evaluate it, and show how it can be optimized for approximate clustering and outlier detection. Finally, we present a thorough experimental evaluation of the proposed method, applying density-biased sampling on real and synthetic data sets, and employing clustering and outlier detection algorithms, thus highlighting the utility of our approach.     

Class conditional distribution alignment for domain adaptation

In this paper, we study the problem of domain adaptation, which is a crucial ingredient in transfer learning with two domains, that is, the source domain with labeled data and the target domain with none or few labels. Domain adaptation aims to extract knowledge from the source domain to improve the performance of the learning task in the target domain. A popular approach to handle this problem is via adversarial training, which is explained by the $\mathcal H \Delta \mathcal H$-distance theory. However, traditional adversarial network architectures just align the marginal feature distribution in the feature space. The alignment of class condition distribution is not guaranteed. Therefore, we proposed a novel method based on pseudo labels and the cluster assumption to avoid the incorrect class alignment in the feature space. The experiments demonstrate that our framework improves the accuracy on typical transfer learning tasks.     

LDA模型下不同分词方法对文本分类性能的影响研究*

通过定义类别聚类密度、类别复杂度以及类别清晰度三个指标,从语料库信息度量的角度研究多种代表性的中文分词方法在隐含概率主题模型LDA下对文本分类性能的影响,定量、定性地分析不同分词方法在网页和学术文献等不同类型文本的语料上进行分类的适用性及影响分类性能的原因。结果表明：三项指标可以有效指明分词方法对语料在分类时产生的影响,Ik Analyzer分词法和ICTCLAS分词法分别受类别复杂度和类别聚类密度的影响较大,二元分词法受三个指标的作用相当,使其对于不同语料具有较好的适应性。对于学术文献类型的语料,使用二元分词法时的分类效果较好,F1值均在80%以上;而网页类型的语料对于各种分词法的适应性更强。本文尝试通过对语料进行信息度量而非单纯的实验来选择提高该语料分类性能的最佳分词方法,以期为网页和学术文献等不同类型的文本在基于LDA模型的分类系统中选择合适的中文分词方法提供参考。     

Non-negative matrix factorization for semi-supervised data clustering

Traditional clustering algorithms are inapplicable to many real-world problems where limited knowledge from domain experts is available. Incorporating the domain knowledge can guide a clustering algorithm, consequently improving the quality of clustering. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for data clustering. In SS-NMF, users are able to provide supervision for clustering in terms of pairwise constraints on a few data objects specifying whether they “must” or “cannot” be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the data similarity matrix to infer the clusters. Theoretically, we show the correctness and convergence of SS-NMF. Moveover, we show that SS-NMF provides a general framework for semi-supervised clustering. Existing approaches can be considered as special cases of it. Through extensive experiments conducted on publicly available datasets, we demonstrate the superior performance of SS-NMF for clustering.

Distance metric learning-based kernel gram matrix learning for pattern analysis tasks in kernel feature space

Approaches to distance metric learning (DML) for Mahalanobis distance metric involve estimating a parametric matrix that is associated with a linear transformation. For complex pattern analysis tasks, it is necessary to consider the approaches to DML that involve estimating a parametric matrix that is associated with a nonlinear transformation. One such approach involves performing the DML of Mahalanobis distance in the feature space of a Mercer kernel. In this approach, the problem of estimation of a parametric matrix of Mahalanobis distance is formulated as a problem of learning an optimal kernel gram matrix from the kernel gram matrix of a base kernel by minimizing the logdet divergence between the kernel gram matrices. We propose to use the optimal kernel gram matrices learnt from the kernel gram matrix of the base kernels in pattern analysis tasks such as clustering, multi-class pattern classification and nonlinear principal component analysis. We consider the commonly used kernels such as linear kernel, polynomial kernel, radial basis function kernel and exponential kernel as well as hyper-ellipsoidal kernels as the base kernels for optimal kernel learning. We study the performance of the DML-based class-specific kernels for multi-class pattern classification using support vector machines. Results of our experimental studies on benchmark datasets demonstrate the effectiveness of the DML-based kernels for different pattern analysis tasks.     

知识迁移的极大熵聚类算法及其在纹理图像分割中的应用

本文研究了一种新型的基于知识迁移的极大熵聚类技术。拟解决两大挑战性问题:1）如何从源域中选择合适的知识对目标域进行迁移学习以最终强化目标域的聚类性能;2）若存在源域聚类数与目标域聚类数不一致的情况时,该如何进行迁移聚类。为此提出一种全新的迁移聚类机制,即基于聚类中心的中心匹配迁移机制。进一步将该机制与经典极大熵聚类算法相融合提出了基于知识迁移的极大熵聚类算法（KT-MEC）。实验表明,在不同迁移场景下的纹理图像分割应用中,KT-MEC算法较很多现有聚类算法具有更高的精确度和抗噪性。     

Categorization of Multiple Objects in a Scene Using a Biased Sampling Strategy

Recently, various bag-of-features (BoF) methods show their good resistance to within-class variations and occlusions in object categorization. In this paper, we present a novel approach for multi-object categorization within the BoF framework. The approach addresses two issues in BoF related methods simultaneously: how to avoid scene modeling and how to predict labels of an image when multiple categories of objects are co-existing. We employ a biased sampling strategy which combines the bottom-up, biologically inspired saliency information and loose, top-down class prior information for object class modeling. Then this biased sampling component is further integrated with a multi-instance multi-label leaning and classification algorithm. With the proposed biased sampling strategy, we can perform multi-object categorization within an image without semantic segmentation. The experimental results on PASCAL VOC2007 and SUN09 show that the proposed method significantly improves the discriminative ability of BoF methods and achieves good performance in multi-object categorization tasks.     

Visually exploring movement data via similarity-based analysis

Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification. We evaluate the proposed similarity measures through an extensive experimental study over synthetic (for measuring efficiency) and real (for assessing effectiveness) trajectory datasets. In particular, the latter could serve as an iterative, combinational knowledge discovery methodology enhanced with visual analytics that provides analysts with a powerful tool for “hands-on” analysis for trajectory data.