Published on Sun Sep 08 2019

Iterative Spectral Method for Alternative Clustering

Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David Kaeli, Jennifer G. Dy

alternative clustering aims to find an alternative partition. We propose a novel method that greatly improves the scalability of KDAC. Our algorithm is intuitive, relies on easily implementable spectral decompositions and comes with theoretical guarantees.

0
0
0
Abstract

Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Alternative Clustering (KDAC). We propose a novel Iterative Spectral Method (ISM) that greatly improves the scalability of KDAC. Our algorithm is intuitive, relies on easily implementable spectral decompositions, and comes with theoretical guarantees. Its computation time improves upon existing implementations of KDAC by as much as 5 orders of magnitude.

Wed Jan 09 2013
Machine Learning
Spectral Clustering Based on Local PCA
We propose a spectral clustering method based on local principal components (PCA) After performing local PCA in selected neighborhoods, the algorithm builds a nearest neighbor graph weighted according to a discrepancy between the principal subspaces in the neighborhoods. We evaluate our algorithm on various simulated data sets.
0
0
0
Thu Nov 22 2007
Machine Learning
Clustering with Transitive Distance and K-Means Duality
Recent spectral clustering methods are a propular and powerful technique for data clustering. These methods need to solve the eigenproblem whose computational complexity is , where is the number of data samples.
0
0
0
Wed Aug 20 2014
Machine Learning
Introduction to Clustering Algorithms and Applications
Data clustering is the process of identifying natural groupings or clusters within multidimensional data. Clustering is a fundamental process in many different disciplines. This paper provides an overview of the different representative clustering methods.
0
0
0
Wed Apr 24 2019
Machine Learning
Construction of the similarity matrix for the spectral clustering method: numerical experiments
Spectral clustering is a powerful method for finding structure in a dataset. It often outperforms traditional clustering algorithms when the structure of the clusters is highly non-convex. Its accuracy depends on how the similarity between pairs of data points is defined.
0
0
0
Sun Nov 23 2014
Machine Learning
A Convex Formulation for Spectral Shrunk Clustering
Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithmsintegrate dimensionality reduction into the clustering process. However, the manifold in a reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space.
0
0
0
Sun Nov 12 2017
Artificial Intelligence
Unified Spectral Clustering with Optimal Graph
Spectral clustering has found extensive use in many areas. It is well-accepted that similarity graph highly affects the clustering results. We propose to automatically learn similarity information from data. We further extend our model to incorporate multiple kernel learning ability.
0
0
0