A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces

• URL: http://arxiv.org/abs/2105.12208v1
• Date: Tue, 25 May 2021 20:44:28 GMT
• Title: A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces
• Authors: Yu Qin, Brittany Terese Fasy, Carola Wenk, and Brian Summa
• Abstract summary: We propose a persistence diagram hashing framework that learns a binary code representation of persistence diagrams. This framework is built upon a generative adversarial network (GAN) with a diagram distance loss function to steer the learning process. Our proposed method is directly applicable to various datasets without the need of retraining the model.
• Score: 7.717214217542406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Persistence diagrams have been widely used to quantify the underlying features of filtered topological spaces in data visualization. In many applications, computing distances between diagrams is essential; however, computing these distances has been challenging due to the computational cost. In this paper, we propose a persistence diagram hashing framework that learns a binary code representation of persistence diagrams, which allows for fast computation of distances. This framework is built upon a generative adversarial network (GAN) with a diagram distance loss function to steer the learning process. Instead of attempting to transform diagrams into vectorized representations, we hash diagrams into binary codes, which have natural advantages in large-scale tasks. The training of this model is domain-oblivious in that it can be computed purely from synthetic, randomly created diagrams. As a consequence, our proposed method is directly applicable to various datasets without the need of retraining the model. These binary codes, when compared using fast Hamming distance, better maintain topological similarity properties between datasets than other vectorized representations. To evaluate this method, we apply our framework to the problem of diagram clustering and we compare the quality and performance of our approach to the state-of-the-art. In addition, we show the scalability of our approach on a dataset with 10k persistence diagrams, which is not possible with current techniques. Moreover, our experimental results demonstrate that our method is significantly faster with less memory usage, while retaining comparable or better quality comparisons.

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