Graph Embedding with Data Uncertainty

Firas Laakom, Jenni Raitoharju, Nikolaos Passalis, Alexandros Iosifidis, Moncef Gabbouj

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Abstract

Spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data. However, most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty. Thus, learning directly from raw data can be misleading and can negatively impact the accuracy. In this paper, we propose to model artifacts in training data using probability distributions; each data point is represented by a Gaussian distribution centered at the original data point and having a variance modeling its uncertainty. We reformulate the Graph Embedding framework to make it suitable for learning from distributions and we study as special cases the Linear Discriminant Analysis and the Marginal Fisher Analysis techniques. Furthermore, we propose two schemes for modeling data uncertainty based on pair-wise distances in an unsupervised and a supervised cons.

Original languageEnglish
Pages (from-to)24232-24239
Number of pages8
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022
Publication typeA1 Journal article-refereed

Keywords

  • dimensionality reduction
  • Graph embedding
  • spectral learning
  • subspace learning
  • uncertainty estimation

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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