Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
Machine Learning
2017-02-24 v1
Abstract
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data. By combining posterior distributions of Gaussian Mixture Models for incomplete data on different scales, we are able to learn a kernel for incomplete data that does not depend on any critical hyperparameters, unlike the commonly used RBF kernel. To evaluate our method, we perform experiments on two real datasets. PCKID outperforms the baseline methods for all fractions of missing values and in some cases outperforms the baseline methods with up to 25 percentage points.
Cite
@article{arxiv.1702.07190,
title = {Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data},
author = {Sigurd Løkse and Filippo Maria Bianchi and Arnt-Børre Salberg and Robert Jenssen},
journal= {arXiv preprint arXiv:1702.07190},
year = {2017}
}