Local distance preserving auto-encoders using Continuous k-Nearest Neighbours graphs

• URL: http://arxiv.org/abs/2206.05909v1
• Date: Mon, 13 Jun 2022 05:38:44 GMT
• Title: Local distance preserving auto-encoders using Continuous k-Nearest Neighbours graphs
• Authors: Nutan Chen, Patrick van der Smagt, Botond Cseke
• Abstract summary: We introduce several auto-encoder models that preserve local distances when mapping from the data space to the latent space. Our method provides state-of-the-art performance across several standard datasets and evaluation metrics.
• Score: 12.607603625414573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Auto-encoder models that preserve similarities in the data are a popular tool in representation learning. In this paper we introduce several auto-encoder models that preserve local distances when mapping from the data space to the latent space. We use a local distance preserving loss that is based on the continuous k-nearest neighbours graph which is known to capture topological features at all scales simultaneously. To improve training performance, we formulate learning as a constraint optimisation problem with local distance preservation as the main objective and reconstruction accuracy as a constraint. We generalise this approach to hierarchical variational auto-encoders thus learning generative models with geometrically consistent latent and data spaces. Our method provides state-of-the-art performance across several standard datasets and evaluation metrics.

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