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                      11月24日講座——中國礦業大學吳鋼教授:A Restarted Large-Scale Spectral Clustering with Self-Guiding and Block Diagonal Representation

                      作者:ykyb  編輯:院科研辦   發布日期: 2023-11-22   來源:院科研辦  

                      講座題目:A Restarted Large-Scale Spectral Clustering with Self-Guiding and Block Diagonal Representation

                      主 講 人:吳鋼 教授(中國礦業大學)

                      講座時間:20231124日(周五)14:00 - 14:45

                      講座地點:錢偉長樓201會議室

                      歡迎有興趣的師生前來聆聽!

                      理學院

                      20231122

                       

                      講座內容簡介:

                      Spectral clustering is one of the most popular unsupervised machine learning methods. Constructing similarity matrix is crucial to this type of method. In most existing works, the similarity matrix is computed once for all or is updated alternatively. However, the former is difficult to reflect comprehensive relationships among data points, and the latter is time-consuming and is even infeasible for large-scale problems. In this work, we propose a restarted clustering framework with self-guiding and block diagonal representation. An advantage of the framework is that some useful clustering information obtained from previous cycles could be preserved as much as possible. To the best of our knowledge, this is the first work that applies this strategy to spectral clustering. The key difference is that we reclassify the samples in each cycle of our method, while they are classified only once in existing methods. To further release the overhead, we introduce a block diagonal representation with Nystr\"{o}m approximation for constructing the similarity matrix. Theoretical results are established to show the rationality of inexact computations in spectral clustering.

                      Comprehensive experiments are performed on some benchmark databases, which show the superiority of our proposed algorithms over many state-of-the-art algorithms for large-scale problems. Specifically, our framework has a potential boost for clustering algorithms and works well even using an initial guess chosen randomly.

                      主講人簡介:

                      吳鋼,博士、中國礦業大學數學學院教授、博士生導師;江蘇省“333工程”中青年科學技術帶頭人,江蘇省“青藍工程”中青年學術帶頭人,江蘇省計算數學學會副理事長。主要研究方向:數值代數、機器學習與數據挖掘、大規??茖W與工程計算等。先后主持國家自然科學基金項目、江蘇省省自然科學基金項目多項,在國際知名雜志,如:SIAM Journal on Numerical Analysis, SIAM Journal on Matrix Analysis and Applications, SIAM Journal on Scientific Computing, IMA Journal of Numerical Analysis, Pattern Recognition, Machine Learning等期刊發表學術論文多篇。

                       

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