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The granularity partition for functional modules is a fundamental research topic in robot distributed control technology. How to evaluate the module partition scheme with different granularity, and then obtain the optimum scheme is the urgent problem. In this paper, we proposed a novel evaluation strategy for the granularity partition of functional modules in robotic system using RTM as control platform...
Nowadays high dimensional data plays an important role in many scientific and research applications. A high dimensional data consists of several features or attributes. These data may contain redundant and irrelevant features. The curse of dimensionality is an important problem in data mining and machine learning. In order to reduce the dimensionality of data and to improve the classifier performance,...
Clustering is a fundamental tool for data analysis. Typically, all attributes of the data are used for clustering. However, if a set of attributes can be divided into meaningful subsets, it may be effective to cluster the data for each subset. In this paper, we propose a method for dividing the set of elements of feature vectors into meaningful subsets. Considering the dependencies between the elements,...
Feature selection is an effective technique for dimensionality reduction to get the most useful information from huge raw data. Many spectral feature selection algorithms have been proposed to address the unsupervised feature selection problem, but most of them fail to pay attention to the noises induced during the feature selection process. In this paper, we not only consider the feature structural...
In recent years, it has become more and more popular to recommend friends on the location-based social network (LBSN), which is combined with the user's behavior in the real world. LBSN has three attributes including temporal, spatial and social correlation. However, the combination situation of the three cannot be solved in previous algorithms. For instance, the problem of recommending friends with...
Linguistic summarization techniques make it easy to gain insight into large amounts of data by describing the main properties of the data linguistically. In this paper we focus on a specific type of data, namely process data, i.e., event logs that contain information about when some activities were performed for a particular customer case. An event log may contain many different sequences, because...
Often in real-world applications such as web page categorization, automatic image annotations and protein function prediction, each instance is associated with multiple labels (categories) simultaneously. In addition, due to the labeling cost one usually deals with a large amount of unlabeled data while the fraction of labeled data points will typically be small. In this paper, we propose a multi-label...
Collaborative filtering provides recommendations based on the behavior of each user combined with behavior of users with similar interests. Recommender systems are becoming widespread, helping people choose movies, books, and things to buy. In this study, we examine the use of Biclustering ARTMAP to build a collaborative filtering recommendation system. We introduce a novel modification to how the...
Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. In this paper, a novel squaring weighted low-rank subspace clustering band selection (SWLRSC) algorithm is proposed for hyperspectral imagery. The SWLRSC method can effectively capture the global structure information of the HSI band set by constructing a strongly connected adjacency...
Starting from scalability to visualization, several challenges come into play when multi-objective optimization algorithms are applied to many-objective optimization problems. These challenges can be tackled using objective reduction algorithms. This work proposes a correlation based objective reduction algorithm. The set of points representing the Pareto-Front are clustered based on correlation distance...
Direction finding cross location is a widely used passive location method, featuring omni-direction and fast speed. However, with the increase in station number, false location points are seen to be rapidly increased among the direction finding lines. In order to accurately and rapidly locate radiation sources, how to rapidly eliminate false points is an important issue urgent to be solved. This paper...
Extracting the motion patterns from videos is a basic task in video surveillance and has become an active research area. In this paper, we propose a novel approach for discovering motion patterns in a scene observed by one or two cameras. The chaos theory is employed to compute the chaotic invariant features (CIFs) after obtaining all the trajectories. The CIFs and other features are combined to a...
Subspace clustering aims to reveal the latent subspace structure underlying high dimensional data by segmenting the data into corresponding subspaces. It has found wide applications in machine learning and computer vision. Most recent works on subspace segmentation focus on subspace representation based methods, which constructs the affinity matrix from the subspace representation of data points....
Recently, social event recommendation, which is to recommend a list of upcoming events to a user, has attracted a lot of research interests. In this paper, we first construct a heterogeneous graph to express the interactions among different types of entities in event-based social network. Based on the constructed graph, we propose a novel recommendation algorithm called reverse random walk with restart...
A measurement method for the evaluation of the image complexity based on SIFT&K-means algorithm, namely the estimation of the mismatch between the target and the interesting points has been introduced in our previous research. Based on this method, we have made some improvements to calculate the image complexity of images with different memory targets. The improved algorithm SIFT&AIM&K-means...
Electrooculography (EOG) enables users to use specific eye movements as inputs for various applications, without using their fingers. However, online classification of such signals often requires long or sophisticated calibration procedures and multiple electrodes, which makes the resulting systems not practical for everyday use. To this end, a single-channel bio-potential recording system was used...
We bring out a runway extraction method based on rotating projection in this paper, which is consisted of three steps, locating the Region of interest (ROI), edge extraction and line detection. Firstly we employed template matching to locate the ROI which contains the runway area. Then we use Sobel operator to extract edges. The rotating projection algorithm is proposed to seek the potential straights...
In this paper, we study the problem of performing multi-label classification on networked data, where each instance in the network is assigned with multiple labels and the connections between instances are driven by various casual reasons. Networked data extracted from social media or web pages may not reflect the relationship between users in real life accurately. By mining the links that actually...
Millions of computers are infected with bot malware, form botnets and enable botmaster to perform malicious and criminal activities. Intrusion Detection Systems are deployed to detect infections, but they raise many correlated alerts for each infection, requiring a large manual investigation effort. This paper presents a novel method with a goal of determining which alerts are correlated, by applying...
Parcellation of brain imaging data is desired for proper neurological interpretation in resting-state functional-magnetic resonance imaging (rs-fMRI) data. Some methods require specifying a number of parcels and using model selection to determine the number of parcels on a rs-fMRI dataset. However, this generalization does not fit with all subjects in a given dataset. A method has been proposed using...
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