Matrix optimization models and algorithms for Data Clustering
报告人：Feiyu Chen（Chongqing Uniersity）
地点：Room 1114, Sciences Building No. 1
Abstract: Clustering is to classify data into groups according to a predefined distance or similarity measure.
It has wide applications in data mining, pattern recognition, image processing and other machine learning areas.
It is well known that lots of clustering models, like K-means and K-indicators, can be written as non-convex
matrix optimization problems.
In this work, we attempt to employ the classical optimization algorithms to solve the unsupervised clustering
task. Numerical examples on several benchmark datasets are conducted to evaluate the effciency and accuracy
of our approach.