An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure

In this paper, we propose a new method for deter-mining shared features of and measuring the distance between data sets or point clouds. Our approach uses the joint factorization of two data matrices X_{1}, X_{2} into non-negative matrices X_{1}=AS_{1}, X_{2}=AS_{2} to derive a similarity measure th...

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Bibliographic Details
Published in2023 57th Annual Conference on Information Sciences and Systems (CISS) pp. 1 - 6
Main Authors Friedman, Hannah, Maina-Kilaas, Amani R., Schalkwyk, Julianna, Ahmed, Hina, Haddock, Jamie
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.03.2023
Subjects
Additive noise
data set distance
Noise measurement
Noise reduction
nonnegative matrix factorization
Plagiarism
Point cloud compression
point cloud distance
Task analysis
topic modeling
Transfer learning
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Summary:In this paper, we propose a new method for deter-mining shared features of and measuring the distance between data sets or point clouds. Our approach uses the joint factorization of two data matrices X_{1}, X_{2} into non-negative matrices X_{1}=AS_{1}, X_{2}=AS_{2} to derive a similarity measure that determines how well the shared basis {A} approximates X_{1}, X_{2} . We also propose a point cloud distance measure built upon this method and the learned factoriI zation. Our method reveals structural differences in both image and text data. Potential applications include classification, detecting plagiarism or other manipulation, data denoising, and transfer learning.
DOI:10.1109/CISS56502.2023.10089765