Graph clustering and other sparse and unsupervised methods for multi-relational data


Graph clustering and other sparse and unsupervised methods for multi-relational data – This paper evaluates the problem of generating and analyzing dense subgraphs of objects of interest in a structured data. The goal of this paper is to develop a framework for such a problem, in which it is possible to make use of structured data from the same or other domain. The framework is based on the notion of a semantic space, which allows for both qualitative and quantitative analyses of the space. The notion of a semantic space can be considered as a function of the structure of the data and can be considered as a function of the structured structure of the data as well as the constraints on the data. The framework also includes the use of structured data as a surrogate for the data in the structured space, which facilitates the integration of structured data with object-oriented tasks. The objective of this paper is to address the problem of learning semantic information from structured data from the same or different domain. Our experimental evaluation results show that semantic clustering can be performed effectively in the structured space.

While the past years have witnessed a dramatic increase of interest in recognizing face images, there have been a number of challenges in this field. To address these challenges, we address the problem of the deep learning based face search. In this work, we propose a novel deep learning based face search method for different purposes, called face recognition based face search. Given the recent success of Face Recognition, our method can be used for the training of supervised and unsupervised face database. While the performance of such deep learning based face search methods is usually comparable to that of unsupervised face search, we show that our approach achieves higher classification accuracy than supervised face search methods while being able to perform comparable tasks to unsupervised face search.

The Dempster-Shafer theory of variance and its application in machine learning

A Novel Color-Headset Feature Extraction Method for Visual Saliency from RGB

Graph clustering and other sparse and unsupervised methods for multi-relational data

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  • An Experimental Comparison of Algorithms for Text Classification

    Deep Learning-Based Facial SearchWhile the past years have witnessed a dramatic increase of interest in recognizing face images, there have been a number of challenges in this field. To address these challenges, we address the problem of the deep learning based face search. In this work, we propose a novel deep learning based face search method for different purposes, called face recognition based face search. Given the recent success of Face Recognition, our method can be used for the training of supervised and unsupervised face database. While the performance of such deep learning based face search methods is usually comparable to that of unsupervised face search, we show that our approach achieves higher classification accuracy than supervised face search methods while being able to perform comparable tasks to unsupervised face search.


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