Local and Global Optimization Techniques in Graph-Based Clustering

Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3456-3464

Abstract


The goal of graph-based clustering is to divide a dataset into disjoint subsets with members similar to each other from an affinity (similarity) matrix between data. The most popular method of solving graph-based clustering is spectral clustering. However, spectral clustering has drawbacks. Spectral clustering can only be applied to macro-average-based cost functions, which tend to generate undesirable small clusters. This study first introduces a novel cost function based on micro-average. We propose a local optimization method, which is widely applicable to graph-based clustering cost functions. We also propose an initial-guess-free algorithm to avoid its initialization dependency. Moreover, we present two global optimization techniques. The experimental results exhibit significant clustering performances from our proposed methods, including 100% clustering accuracy in the COIL-20 dataset.

Related Material


[pdf]
[bibtex]
@InProceedings{Ikami_2018_CVPR,
author = {Ikami, Daiki and Yamasaki, Toshihiko and Aizawa, Kiyoharu},
title = {Local and Global Optimization Techniques in Graph-Based Clustering},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}