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Home / programming language / Online Java Projects / Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight
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Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight

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SKU: PROJ8207 Categories: 2018-2019 Projects, Datamining Projects, Online Java Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Matlab Project, PHP Project, Students Projects
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Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight

Abstract– In the first place, With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. At the same time, the difficulty of obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. In spite of, Traditional approaches all characterize the similarity by fixed and pre-defined graph Laplacian in each view separately and ignore the underlying common structures across different views. In algorithm named Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight (ASVW) to overcome the above mentioned problems. Specifically, by leveraging the learning mechanism to characterize the common structures adaptively. Rather, the objective function by a common graph Laplacian across different views, together with the sparse â„“2;p -norm constraint designed for feature selection. We develop an efficient algorithm to address the non-smooth minimization problem and prove that the algorithm will converge. Under those circumstances, validate the effectiveness of ASVW, comparisons are made with some benchmark methods on real-world datasets. As a result, in the real sports action recognition task. The experimental results demonstrate the effectiveness of our proposed algorithm.

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