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Learning now occurs in various manners in social networks, utilizing practice communities and learning networks. In this context, students are interested in exploring learning activities of other students without having to read through large quantities of textual content. Students tend to be interested in finding information concerning their majors, contents of their subjects and their co-learners...
This paper presents an improvement of the ELMVIS+ method that is proposed for fast nonlinear dimensionality reduction. The ELMVIS++C has an additional supervised learning component compared to ELMVIS+, which is originally an unsupervised method as like the majority of the other dimensionality reduction method. This component prevents samples under the same class being separated apart from each other...
Information visualization is essential for improving effectiveness and efficiency of data exploration and knowledge discovery. Therefore, visualization has been used in a wide range of fields from biology, medicine, criminal activity analysis to business and education. Information visualization has become more important than ever as the amount of data being generated has increased dramatically in...
In this paper, we presented a spark based analysis framework for large scale spatial temporal data such as trajectories, LBS, Noise distribution and so on. With spark, spatial temporal data can be processed, mined and the results can be saved in parallel. Then these results will be imported into spatial-indexed databases for further applications. Finally, we studied the volume rendering methods to...
Deep neural networks have been used successfully for several different computer vision-related tasks, including facial expression recognition. In spite of the good results, it is still not clear why these networks achieve such good recognition rates. One way to learn more about deep neural networks is to visualise and understand what they are learning, and to do so techniques such as deconvolution...
Unsupervised learning aims to discovery latent representation embedded in the observation, which is useful for data visualization, dimensionality reduction, and density modeling. Autoencoders have been successfully used to learn the latent variations in data, especially with the recent reintroduction by deep learning. For some specific tasks, there are supervised information or labels that can be...
Fundamental challenges and goals of the cognitive algorithms are moving super-intelligent machines and super-intelligent humans from dreams to reality. This paper is devoted to a technical way to reach some specific aspects of super-intelligence that are beyond the current human cognitive abilities. Specifically the proposed technique is to overcome inabilities to analyze a large amount of abstract...
In this paper we deal with one of the most relevant problems in the field of data mining, the real time processing and visualization of data streams. To deal with data streams we propose a novel approach that uses a neighborhood-based clustering. Instead of processing each new element one by one, we propose to process each group of new elements simultaneously. A clustering is applied on each new group...
The abundance of computing and mobile devices makes the problem of user identification and verification an essential requirement for many applications. Haptics devices include the sense of touch in the form of kinesthetic and tactile feedback which provide additional features within handwritten signatures. However, they generate high dimensional data and dimensionality reduction techniques become...
Generative Topographic Mapping (GTM) is a popular probabilistic framework for modeling non-linear relationships in high-dimensional data as well as for unsupervised learning and visualization of such data. It is also known as to provide a principled probabilistic alternative to the well-known Self-Organizing Map (SOM) in the neural networks community, thanks to its flexible mixture model formulation...
Feature selection is used to preserve significant properties of data in a compact space. In particular, feature selection is needed in applications, where information comes from multiple heterogeneous high dimensional sources. Data integration, however, is a challenge in itself.
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