Affiliation:
1. University of Science and Technology of China, Hefei, Anhui, China
2. HEC, University of Lausanne, Switzerland
Abstract
Traditional embedding methodologies, also known as dimensionality reduction techniques, assume the availability of exact pairwise distances between the high-dimensional objects that will be embedded in a lower dimensionality. In this article, we propose an embedding that overcomes this limitation and can operate on pairwise distances that are represented as a range of lower and upper bounds. Such bounds are typically estimated when objects are compressed in a lossy manner, so our approach is highly applicable in the case of big compressed datasets. Our methodology can preserve multiple aspects of the original data relationships: distances, correlations, and object scores/ranks, whereas existing techniques typically preserve only distances. Comparative experiments with prevalent embedding methodologies (ISOMAP, t-SNE, MDS, UMAP) illustrate that our approach can provide fidelitous preservation of multiple object relationships, even in the presence of inexact distance information. Our visualization method is also easily interpretable.
Funder
Ministry of Science and Technology of China
Anhui Dept. of Science and Technology
Toward Interpretable Machine Learning
Publisher
Association for Computing Machinery (ACM)
Cited by
1 articles.
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