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Datasets obtained through recently advanced measurement techniques tend to possess a large number of dimensions. This leads to explosively increasing computation costs for analyzing such datasets, thus making formulation and verification of scientific hypotheses very difficult. Therefore, an efficient approach to identifying feature subspaces of target datasets, that is, the subspaces of dimension...
Twitter, a well-liked online social networking site, facilitates millions of users on a daily basis to dispatch and orate quick 140-character notes named tweets. Nowadays, Twitter is cogitated as the fastest and popular intermediate of communication and is used to follow latest events. Tweets pertaining to a specific event can be effortlessly found using keyword matching, but there are numerous tweets...
We present a new multi-level graph drawing algorithm based on the k-core coarsening, a well-known cohesive subgroup analysis method in social network analysis. The k-core of a graph is also known as the degeneracy in graph theory, and can be computed in linear time. Our k-core based multi-level algorithm also includes a new concentric circle placement and a variation of force-directed layout to display...
Given a graph structure, different layout algorithms (even different settings of the same algorithm) usually result in different arrangements of vertices, and each layout may reflect certain aspects/parts of the graph more accurately than others. Thus, for high-level graph analysis tasks that rely on the overall arrangement of vertices, drawing conclusions only from one layout is risky. To alleviate...
In this paper, we discuss proportional data clustering. It emerges In many applications such as document clustering and Image classification using bag of visual words approach. When deploying mixture models for clustering, there Is always a problem of initialization, and It Is common to initialize using K-means algorithm. In proposed work, we present K-means clustering approach using different distance...
Spatio-temporal anomaly detection by unsupervised learning have applications in a wide range of practical settings. In this paper we present a surveillance system for industrial robots using a monocular camera. We propose a new unsupervised learning method to train a deep feature extractor from unlabeled images. Without any data augmentation, the algorithm co-learns the network parameters on different...
Object clustering is a very challenging unsupervised learning problem in machine learning and pattern recognition. In this paper, we will study visual object pattern clustering problem by combining the k-means clustering algorithm and the binary sketch templates, which quantify each image by a vector of indicators showing that a sketch at certain location, scale, and orientation exist or not. This...
In this paper, we present a method for vision-based place recognition in environments with a high content of similar features and that are prone to variations in illumination. The high similarity of features makes difficult the disambiguation between two different places. The novelty of our method relies on using the Bag of Words (BoW) approach to derive an image descriptor from a set of relevant...
We introduce a novel bags-of-features framework based on relative position descriptors, modeling both spatial relations and shape information between the pairwise structural subparts of objects. First, we propose a hierarchical approach for the decomposition of complex objects into structural subparts, as well as their description using the concept of Force Histogram Decomposition (FHD). Then, an...
Trajectory segmentation is the process of subdividing a trajectory into parts either by grouping points similar with respect to some measure of interest, or by minimizing a global objective function. Here we present a novel online algorithm for segmentation and summary, based on point density along the trajectory, and based on the nature of the naturally occurring structure of intermittent bouts of...
Multidimensional Scaling (MDS) is one of the most versatile tools used for exploratory data mining. It allows a first glimpse of possible structure in the data, which can inform the choice of analyses used. Its uses are multiple. It can give the user an idea as to the clusterability or linear separability of the data. It can help spot outliers, or can hint at the intrinsic dimensionality of the data...
The availability of large-scale network data has given rise to the opportunity to investigate higher level organization of these networks using graph theoretic analysis. In this paper, we demonstrate a novel network decomposition tool called FacetsViewer in order to make sense of the deluge of network data. In contrast to traditional graph clustering techniques, it finds not just a single decomposition...
Currently, the supervised trained deep neural networks (DNNs) have been successfully applied in several image classification tasks. However, how to extract powerful data representations and discover semantic concepts from unlabeled data is a more practical issue. Unsupervised feature learning methods aim at extracting abstract representations from unlabeled data. Large amount of research works illustrate...
A Self-Enforcing Network (SEN), which is a self-organized neural network, is introduced to cluster medical data. In addition, a cue validity factor is defined, which affects the clustering of the data. The results show that a user can influence the clustering of data by SEN, thus allowing the analysis of the data depending on economical, medical or nursing interests. The described prototype includes...
User-generated content on online social media (OSM) has several data mining applications, such as extracting useful information during disaster events. Since popular / important content is often re-posted by multiple people on OSM, identifying duplicate content is an important first step in many data mining applications. In this work, we develop a methodology to identify near-duplicate images posted...
Historically, eye tracking systems have been a very useful tool for finding salient regions in interfaces that naturally attract the visual attention of users. Scan paths are created as the eye moves from one salient region to another. Research has shown that a relationship exists between scan path direction and cognitive load when navigating a user interface. The analysis of scan paths during interface...
Figure detection, separation and image classification are common problems occurring in various fields, especially medicine. Since image databases are usually large, manual classification would be a demanding task. In this paper, we proposed a method for automatic compound figure detection and separation, and gave a comparison between other recognition methods, such as convolutional neural networks...
Robust and accurate lip segmentation is a significant and fundamental procedure for visual speech analysis. Compared with segmenting a closed mouth from the background, lip segmentation with open mouth is a more challenging task due to the complex components inside the mouth (i.e. teeth, oral cavity, etc.), and most of the current techniques fail to show all the inner mouth details clearly. To deal...
We describe a matrix-based visualization technique for algorithmically and visually comparing metrics in eye movement data. To reach this goal, a set of scanpath trajectories is first preprocessed andtransformedintoasetofmetricsdescribingcommonalitiesand differences of eye movement trajectories. To keep the generated diagrams simple, understandable, and free of visual clutter we visuallyencodethegenerateddatasetintothecellsofamatrix...
We here present parts of our ongoing work to facilitate the largescale analysis of smooth pursuit eye movements made while viewing dynamic natural scenes. Classification of smooth pursuit episodes can be difficult in the presence of eye-tracking noise, and we thus recently proposed an algorithm that clusters gaze recordings from several observers in order to improve classification robustness. We now...
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