Graph Embedding With Data Uncertainty

Firas Laakom

doi:10.1109/ACCESS.2022.3155233

Graph Embedding With Data Uncertainty

2022-01-01

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DOI：10.1109/ACCESS.2022.3155233

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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 contexts.

Keywords:

Uncertainty

Data models

Principal component analysis

Optimization

Gaussian distribution

Eigenvalues and eigenfunctions

Training data

Graph embedding

subspace learning

dimensionality reduction

uncertainty estimation

spectral learning

Graph Embedding With Data Uncertainty

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