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In this paper, given data with high-dimensional features, we study this problem of how to calculate the similarity between two samples by considering feature interaction network, where a feature interaction network represents the relationship between features. This is different from some traditional methods, those of which learn similarities based on a sample network that represents the relationship between samples. Therefore, we propose a novel network-based similarity metric for computing the similarity between samples, which incorporates the knowledge of feature interaction network, in order to overcome the data sparseness problem. Our similarity metric uses a new Feature Alignment Similarity measure, which does not directly compute the similarities among samples, but projects each sample into a feature interaction network and measures the similarities between two samples using the similarities between the vertices of the samples in the network. As such, when two samples do not share any common features, they are likely to have higher similarity values when their features share the similar network regions. For ensuring that the metric is useful in a real-world application, we apply our metric to discover subtypes in tumor mutational data by incorporating the information of the gene interaction network. Our experimental results from using synthetic data and real-world tumor mutational data show that our approach outperforms the top competitors in cancer subtype discovery. Furthermore, our approach can identify cancer subtypes that cannot be detected by other clustering algorithms in real cancer data.

Automated change-point detection of EEG signals is becoming essential for the monitoring of health behaviors and health status in a wide range of clinical applications. This paper presents a structural time-series analysis to capture and characterize the dynamic behavior of EEG signals, and develops a method to detect the EEG change points. For a given EEG signal, the proposed method is operated as follows: 1) a sub-band pass filter is fist designed to capture those frequency components that can characterize the dynamic behavior of the data, and the so-called power spectrum is extracted as the EEG features; 2) together with a sliding-window technique, an automatic `segment-to-segment' analysis of EEG signal, is developed with a null hypothesis testing for decision making. In particular, the main challenge of the proposed method is to design an appropriate distance metric that is compatible with our considered data/problem. To achieve this end, we first collect a variety of metrics from other areas that would be potentially available for our problem, and then compare them for the considered EEG change point detection. Experiments are conducted on two different data sets. Results show the Bhattacharyya distance achieves the best detection result among all investigated metrics. Meanwhile, comparison with state-of-the-arts demonstrates the effectiveness of the method in real applications.

Although mutual information is a general method usually being used to measure the similarity of two images, the robustness is questionable due to the absence of spatial information. The purpose of this study is to develop a feasible sorting technique for 4D-CT. A novel sorting algorithm named mutual information and edge gradient (MIEG), which includes spatial information by combining mutual information with a term based on the edge gradient of the image, was proposed to sort sequential CT images. The edge of image was extracted by calculating the wavelet transform modulus maxima, and the gradient similarity coefficient of the edge image was calculated and used to multiply by mutual information to form the final similarity metric. This sorting technique was validated by comparing the 4D-CTs reconstructed using MIEG and Real-time Position Management system (Varian Medical Systems, Inc., Palo Alto, CA). Tumor motion trajectories derived from 4D-CTs were analyzed in three orthogonal directions. Their correlation coefficients (CC) and differences in tumor motion magnitude (Ds) were determined. In addition, Dice similarity coefficient (DSC) was used to measure how well the tumor volumes segmented from the two 4D datasets overlapped with each other. For all patients, the mean CC values were >0.95 in all directions. The mean Ds were

Reconstructing 3D point cloud models from image sequences tends to be impacted by illumination variations and textureless cases in images, resulting in missing parts or uneven distribution of retrieved points. To improve the reconstructing completeness, this work proposes an enhanced similarity metric which is robust to illumination variations among images during the dense diffusions to push the seed-and-expand reconstructing scheme to a further extent. This metric integrates the zero-mean normalized cross-correlation coefficient of illumination and that of texture information which respectively weakens the influence of illumination variations and textureless cases. Incorporated with disparity gradient and confidence constraints, the candidate image features are diffused to their neighborhoods for dense 3D points recovering. We illustrate the two-phase results of multiple datasets and evaluate the robustness of proposed algorithm to illumination variations. Experiments show that ours recovers 10.0% more points, on average, than comparing methods in illumination varying scenarios and achieves better completeness with comparative accuracy.

Nowadays, people usually participate in multiple social networks simultaneously, e.g., Facebook and Twitter. Formally, the correspondences of the accounts that belong to the same user are defined as anchor links, and the networks aligned by anchor links can be denoted as aligned networks. In this paper, we study the problem of anchor link prediction (ALP) across a pair of aligned networks based on social network structure. First, three similarity metrics (CPS, CCS, and CPS+) are proposed. Different from the previous works, we focus on the theoretical guarantees of our metrics. We prove mathematically that the node pair with the maximum CPS or CPS+ should be an anchor link with high probability and a correctly predicted anchor link must have a high value of CCS. Second, using the CPS+ and CCS, we present a two-stage iterative algorithm CPCC to solve the problem of the ALP. More specifically, we present an early termination strategy to make a tradeoff between precision and recall. At last, a series of experiments are conducted on both synthetic and real-world social networks to demonstrate the effectiveness of the CPCC.

International audience; Detecting community structure discloses tremendous information about complex networks and unlock promising applied perspectives. Accordingly, a numerous number of community detection methods have been proposed in the last two decades with many rewarding discoveries. Notwithstanding, it is still very challenging to determine a suitable method in order to get more insights into the mesoscopic structure of a network given an expected quality, especially on large scale networks. Many recent efforts have also been devoted to investigating various qualities of community structure associated with detection methods, but the answer to this question is still very far from being straightforward. In this paper, we propose a novel approach to estimate the similarity between community detection methods using the size density distributions of communities that they detect. We verify our solution on a very large corpus of networks consisting in more than a hundred networks of five different categories and deliver pairwise similarities of 16 state-of-the-art and well-known methods. Interestingly, our result shows that there is a very clear distinction between the partitioning strategies of different community detection methods. This distinction plays an important role in assisting network analysts to identify their rule-of-thumb solutions.

Deep analysis of radiographic images can quantify the extent of intra-tumoral heterogeneity for personalized medicine. In this paper, we propose a novel content-based multi-feature image retrieval (CBMFIR) scheme to discriminate pulmonary nodules benign or malignant. Two types of features are applied to represent the pulmonary nodules. With each type of features, a single-feature distance metric model is proposed to measure the similarity of pulmonary nodules. And then, multiple single-feature distance metric models learned from different types of features are combined to a multi-feature distance metric model. Finally, the learned multi-feature distance metric is used to construct a content-based image retrieval (CBIR) scheme to assist the doctors in diagnosis of pulmonary nodules. The classification accuracy and retrieval accuracy are used to evaluate the performance of the scheme. The classification accuracy is 0.955 ± 0.010, and the retrieval accuracies outperform the comparison methods. The proposed CBMFIR scheme is effective in diagnosis of pulmonary nodules. Our method can better integrate multiple types of features from pulmonary nodules.

The case-based reasoning (CBR) approach is a modern approach. It is adopted for designing knowledgebased expertise systems. The aforementioned approach depends much on the stored experiences. These experiences serve as cases that can be employed for solving new problems. That is done through retrieving similar cases from the system and utilizing their solutions. The latter approach aims at solving problems through reviewing, processing and applying their experiences. The present study aimed to shed a light on a CBR application. That is done to develop a new system for assisting Internet users and solving the problems faced by those users. In addition, the present study focuses on the cases retrieval stage. It aimed at designing and building an experienced inquiry system for solving any problem that internet users might face when using a case-based reasoning (CBR) dialogue. That shall enable internet users to solve the problems faced when using the Internet. The system that was developed by the researcher operates through displaying the similar cases through a dialogue. That is done through using a well-developed algorithm and reviewing the relevant previous studies. It was found that the success rate of the proposed system is high.The case-based reasoning (CBR) approach is a modern approach. It is adopted for designing knowledgebased expertise systems. The aforementioned approach depends much on the stored experiences. These experiences serve as cases that can be employed for solving new problems. That is done through retrieving similar cases from the system and utilizing their solutions. The latter approach aims at solving problems through reviewing, processing and applying their experiences. The present study aimed to shed a light on a CBR application. That is done to develop a new system for assisting Internet users and solving the problems faced by those users. In addition, the present study focuses on the cases retrieval stage. It aimed at designing and building an experienced inquiry system for solving any problem that internet users might face when using a case-based reasoning (CBR) dialogue. That shall enable internet users to solve the problems faced when using the Internet. The system that was developed by the researcher operates through displaying the similar cases through a dialogue. That is done through using a well-developed algorithm and reviewing the relevant previous studies. It was found that the success rate of the proposed system is high.

In this paper, we introduce a deformation based representation space for curved shapes in $\mathbb {R}^{n}$ . Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an element of a finite dimensional matrix Lie group. Variation due to scale and location are filtered in a preprocessing stage, while shapes that vary only in rotation are identified by an equivalence relationship. The use of a finite dimensional matrix Lie group leads to a similarity metric with an explicit geodesic solution. Subsequently, we discuss some of the properties of the metric and its relationship with a deformation by least action . Furthermore, invariance to reparametrization or estimation of point correspondence between shapes is formulated as an estimation of sampling function. Thereafter, two possible approaches are presented to solve the point correspondence estimation problem. Finally, we propose an adaptation of k-means clustering for shape analysis in the proposed representation space. Experimental results show that the proposed representation is robust to uninformative cues, e.g., local shape perturbation and displacement. In comparison to state of the art methods, it achieves a high precision on the Swedish and the Flavia leaf datasets and a comparable result on MPEG-7, Kimia99 and Kimia216 datasets.

In recent years, with the development of the Internet of Things (IoT) technology, a large amount of data can be captured from sensors for real-time analysis. By monitoring the traffic video data from the IoT, we can detect the anomalies that may occur and evaluate the security. However, the number of traffic anomalies is extremely limited, so there is a severe over-fitting problem when using traditional deep learning methods. In order to solve the problem above, we propose a similarity metric Convolutional Neural Network (CNN) based on a channel attention model for traffic anomaly detection task. The method mainly includes (1) A Siamese network with a hierarchical attention model by word embedding so that it can selectively measure similarities between anomalies and the templates. (2) A deep transfer learning method can automatically annotate an unlabeled set while fine-tuning the network. (3) A background modeling method combining spatial and temporal information for anomaly extraction. Experiments show that the proposed method is three percentage points higher than deep convolutional generative adversarial network (DCGAN) and five percentage points higher than AutoEncoder on the accuracy. No more time consumption is needed for the annotation process. The extracted candidates can be classified correctly through the proposed method.

In order to study software evolution, it is necessary to measure artefacts representative of project releases. If we consider the process of software evolution to be copying with subsequent modification, then, by analogy, placing emphasis on what remains the same between releases will lead to focusing on similarity between artefacts. At the same time, software artefacts–stored digitally as binary strings–are all information. This paper introduces a new method for measuring software evolution in terms of artefacts’ shared information content. A similarity value representing the quantity of information shared between artefact pairs is produced using a calculation based on Kolmogorov complexity. Similarity values for releases are then collated over the software’s evolution to form a map quantifying change through lack of similarity. The method has general applicability: it can disregard otherwise salient software features such as programming paradigm, language or application domain because it considers software artefacts purely in terms of the mathematically justified concept of information content. Three open-source projects are analysed to show the method’s utility. Preliminary experiments on udev and git verify the measurement of the projects’ evolutions. An experiment on ArgoUML validates the measured evolution against experimental data from other studies.

The user's knowledge plays a pivotal role in the usability and experience of any information system. Based on a semantic network and query logs, this paper introduces a metric for users' knowledge on a topic. The finding that people often return to several sets of closely related, well-known, topics, leading to certain concentrated, highly activated areas in the semantic network, forms the core of this metric. Tests were performed determining the knowledgeableness of 32,866 users on in total 8 topics, using a data set of more than 6 million queries. The tests indicate the feasibility and robustness of such a user-centered indicator.

Nonrigid multimodal image registration remains a challenging task in medical image processing and analysis. The structural representation (SR)-based registration methods have attracted much attention recently. However, the existing SR methods cannot provide satisfactory registration accuracy due to the utilization of hand-designed features for structural representation. To address this problem, the structural representation method based on the improved version of the simple deep learning network named PCANet is proposed for medical image registration. In the proposed method, PCANet is firstly trained on numerous medical images to learn convolution kernels for this network. Then, a pair of input medical images to be registered is processed by the learned PCANet. The features extracted by various layers in the PCANet are fused to produce multilevel features. The structural representation images are constructed for two input images based on nonlinear transformation of these multilevel features. The Euclidean distance between structural representation images is calculated and used as the similarity metrics. The objective function defined by the similarity metrics is optimized by L-BFGS method to obtain parameters of the free-form deformation (FFD) model. Extensive experiments on simulated and real multimodal image datasets show that compared with the state-of-the-art registration methods, such as modality-independent neighborhood descriptor (MIND), normalized mutual information (NMI), Weber local descriptor (WLD), and the sum of squared differences on entropy images (ESSD), the proposed method provides better registration performance in terms of target registration error (TRE) and subjective human vision.

Similarity and dissimilarity measures are widely used in many research areas and applications. When a dissimilarity measure is used, it is normally required to be a distance metric. However, when a similarity measure is used, there is no formal requirement. In this article, we have three contributions. First, we give a formal definition of similarity metric. Second, we show the relationship between similarity metric and distance metric. Third, we present general solutions to normalize a given similarity metric or distance metric.

An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse. ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

peer-reviewed Many authors have noted the problem of excessive information when attempting to create useful visualisations of software. The problem of visualising change over multiple versions of software is more complex still. We present a means of visualising changes in software, founded on information-theoretic arguments, that easily and automatically summarises difference between software versions with respect to their code, their structure or their behaviour. Further, we show, by creating visualisations in experiments on real-world data, that the method is of utility to practitioners and has implications beyond the field of software visualisation.

This paper describes a quantitative similarity metric and its contribution to achieve original plan solutions. This similarity metric is used by an iterative process of piece retrieval from structured plan cases. Within our approach plan cases are tree-like networks of pieces (goals and actions). These case pieces are ill-related each other by links (explanations). These links may be classified as hierarchical or temporal, antecedent or consequent, and explicit or implicit. Besides links, each case piece has also information about its properties (the attributes-value pairs), its hierarchical and temporal position in the case (the address), and about its constraints in the relationship with others (the constraints). The similarity metric computes a similarity value between two case pieces taking into account similarities between these case piece's information types. Each time a problem is proposed, different weights are given to some of those similarities, with the aim of solving it with an original solution. This similarity metric is used by the system INSPIRER (ImagiNation taking as Source Past and Imperfectly REalated Reasonings). We illustrate the role of the similarity metric in the creativity of solutions, focusing specially their originality, with the presentation of the experimental results obtained in the musical composition domain, which is considered by us as a planning domain.