Multi-kernel graph fusion for spectral clustering |
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| Institution: | 1. West China Biomedical Big Data Center, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;2. Department of Radiology, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;3. West China Periodicals, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;4. Department of Bile Duct Surgery, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;1. School of Computing and Information, University of Pittsburgh, Pittsburgh, Pennsylvania, USA;2. School of Information Science, University of Kentucky, Lexington, Kentucky, USA;3. School of Nursing, The University of Texas at Austin, Austin, Texas, USA;4. School of Information, The University of Texas at Austin, Austin, Texas, USA;1. School of Information Management, Nanjing University, Nanjing 210023, China;2. School of Computer Science and Engineering, Southeast University, Nanjing 210096, China;3. School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China;1. School of Information, Florida State University, Tallahassee, Florida USA;2. College of Medicine, Florida State University, Tallahassee, Florida USA;3. Department of Statistics, Florida State University, Tallahassee, Florida USA;4. Department of Computer Science, Florida State University, Tallahassee, Florida USA;5. Department of Psychology, Florida State University, Tallahassee, Florida USA;6. Department of Psychology, University of Central Florida, Orlando, Florida USA;7. Department of Psychology, The University of Alabama, Tuscaloosa, Alabama USA |
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| Abstract: | Many methods of multi-kernel clustering have a bias to power base kernels by ignoring other kernels. To address this issue, in this paper, we propose a new method of multi-kernel graph fusion based on min–max optimization (namely MKGF-MM) for spectral clustering by making full use of all base kernels. Specifically, the proposed method investigates a novel min–max weight strategy to capture the complementary information among all base kernels. As a result, every base kernel contributes to the construction of the fusion graph from all base kernels so that the quality of the fusion graph is guaranteed. In addition, we design an iterative optimization method to solve the proposed objective function. Furthermore, we theoretically prove that our optimization method achieves convergence. Experimental results on real medical datasets and scientific datasets demonstrate that the proposed method outperforms all comparison methods and the proposed optimization method achieves fast convergence. |
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| Keywords: | Multi-kernel learning Affinity graph Self-expression Spectral clustering |
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