Twin Learning for Similarity and Clustering: A Unified Kernel Approach

05/01/2017
by   Zhao Kang, et al.
0

Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However, similarity measurement is challenging because it is usually impacted by many factors, e.g., the choice of similarity metric, neighborhood size, scale of data, noise and outliers. Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering. In addition, nonlinear similarity often exists in many real world data which, however, has not been effectively considered by most existing methods. To tackle these two challenges, we propose a model to simultaneously learn cluster indicator matrix and similarity information in kernel spaces in a principled way. We show theoretical relationships to kernel k-means, k-means, and spectral clustering methods. Then, to address the practical issue of how to select the most suitable kernel for a particular clustering task, we further extend our model with a multiple kernel learning ability. With this joint model, we can automatically accomplish three subtasks of finding the best cluster indicator matrix, the most accurate similarity relations and the optimal combination of multiple kernels. By leveraging the interactions between these three subtasks in a joint framework, each subtask can be iteratively boosted by using the results of the others towards an overall optimal solution. Extensive experiments are performed to demonstrate the effectiveness of our method.

READ FULL TEXT
research
11/12/2017

Unified Spectral Clustering with Optimal Graph

Spectral clustering has found extensive use in many areas. Most traditio...
research
05/25/2018

Scalable Spectral Clustering Using Random Binning Features

Spectral clustering is one of the most effective clustering approaches t...
research
02/04/2019

Self-Tuning Spectral Clustering for Adaptive Tracking Areas Design in 5G Ultra-Dense Networks

In this paper, we address the issue of automatic tracking areas (TAs) pl...
research
08/28/2019

Similarity Kernel and Clustering via Random Projection Forests

Similarity plays a fundamental role in many areas, including data mining...
research
05/21/2019

Spatially Constrained Spectral Clustering Algorithms for Region Delineation

Regionalization is the task of dividing up a landscape into homogeneous ...
research
04/25/2019

Discrete Optimal Graph Clustering

Graph based clustering is one of the major clustering methods. Most of i...
research
10/17/2019

A Unified Framework for Tuning Hyperparameters in Clustering Problems

Selecting hyperparameters for unsupervised learning problems is difficul...

Please sign up or login with your details

Forgot password? Click here to reset