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  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
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      저자 : Jiangshu Wei,Mantao Wang,Qianqian Wu (검색결과 2 건)
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      • Research on Different Representation Methods for Classification

        Jiangshu Wei,Xiangjun Qi,Mantao Wang 보안공학연구지원센터 2014 International Journal of Multimedia and Ubiquitous Vol.9 No.12

        Under today’s big data environment, with the rapid development of computer network technology and information technology, data mining is becoming more and more important in computer science. Classification is one of the most important aspects in data mining research Field. Recently, representation methods, such as sparse representation and low rank representation, have been much concerned. They both have wide applications in scientific and engineering fields. However, sparse representation and low rank representation include many methods, although these methods have their own characteristics, they are all effective for handling classification problems. This paper focuses on the performance comparison of different representation methods currently used in handling classification problems and views other conventional methods that can be applied in this field.

      • Study on Different Representation Methods for Subspace Segmentation

        Jiangshu Wei,Mantao Wang,Qianqian Wu 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.1

        With many engineering and science application problems, we must deal with a lot of high-dimensional data, such as videos, images, web documents, text, etc. In the areas of computer vision, image processing and machine learning, high-dimensional data are widespread. However, it is very hard for obtaining meaningful learning and inference from these high-dimensional data directly, the computational complexity of high-dimensional data is often exponential. However, under many conditions, high-dimensional data lie in low-dimensional data corresponding to some classes of the data. Thus, finding the low-dimensional structure from the high-dimensional data is very important. The aim of subspace segmentation is to cluster data that lie in a union of low-dimensional subspaces. In recent years, based on the research of representation methods, many subspace segmentation algorithms appeared. Although these methods are all effective for handling subspace segmentation problems, they all have advantages and disadvantages. This paper focuses on the performance comparison of different subspace segmentation algorithms currently used in handling subspace segmentation problems and views other conventional methods that can be applied in this field.

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