A new eigenvector selection strategy applied to develop spectral clustering

作者：Hosseini M^*; Azar F Torkamani

来源：Multidimensional Systems and Signal Processing, 2017, 28(4): 1227-1248.

DOI：10.1007/s11045-016-0391-6

• High-dimensional data
• Curse of dimensionality
• Spectral clustering
• Pattern recognition

Spectral methods are strong tools that can be used for extraction of the data's structure based on eigenvectors of constructed affinity matrices. In this paper, we aim to propose some new measurement functions to evaluate the ability of each eigenvector of affinity matrix in data clustering. In the proposed strategy, each eigenvector's elements are clustered by traditional fuzzy c-means algorithm and then informative eigenvectors selection is performed by optimization of an objective function which defined based on three criterions. These criterions are the compactness of clusters, distance between clusters and stability of clustering to evaluate each eigenvector based on considering the structure of clusters which placed on. Finally, Lagrange multipliers method is used to minimize the proposed objective function and extract the most informative eigenvectors. To indicate the merits of our algorithm, we consider UCI Machine Learning Repository databases, COIL20, YALE-B and PicasaWeb as benchmark data sets. Our simulation's results confirm the superior performance of the proposed strategy in developing spectral clustering compared to conventional clustering methods and recent eigenvector selection based algorithms.

• 出版日期2017-10