Mixture of Gaussians for Distance Estimation with Missing Data
Abstract
Many Data Sets Have Missing Values in Practical Application Contexts, But the Majority of Commonly Studied Machine Learning Methods Cannot Be Applied Directly When There Are Incomplete Samples. However, Most Such Methods Only Depend on the Relative Differences between Samples Instead of their Particular Values, and Thus One Useful Approach is to Directly Estimate the Pairwise Distances between All Samples in the Data Set. This is Accomplished by Fitting a Gaussian Mixture Model to the Data, and using It to Derive Estimates for the Distances. a Variant of the Model for High-Dimensional Data with Missing Values is Also Studied. Experimental Simulations Confirm that the Proposed Method Provides Accurate Estimates Compared to Alternative Methods for Estimating Distances. in Particular, using the Mixture Model for Estimating Distances is on Average More Accurate Than using the Same Model to Impute Any Missing Values and Then Calculating Distances. the Experimental Evaluation Additionally Shows that More Accurately Estimating Distances Lead to Improved Prediction Performance for Classification and Regression Tasks When Used as Inputs for a Neural Network. © 2013 Elsevier B.v.
Recommended Citation
E. Eirola et al., "Mixture of Gaussians for Distance Estimation with Missing Data," Neurocomputing, vol. 131, pp. 32 - 42, Elsevier, May 2014.
The definitive version is available at https://doi.org/10.1016/j.neucom.2013.07.050
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Distance estimation; Missing data; Mixture model
International Standard Serial Number (ISSN)
1872-8286; 0925-2312
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
05 May 2014
