ToFU: Topology functional units for deep learning

Christopher Oballe; David Boothe; Piotr J. Franaszczuk; Vasileios Maroulas

doi:10.3934/fods.2021021

Article Contents

2022, Volume 4, Issue 4: 641-665. Doi: 10.3934/fods.2021021

This issue Previous Article A density-based approach to feature detection in persistence diagrams for firn data Next Article Multiple hypothesis testing with persistent homology

ToFU: Topology functional units for deep learning

1.
Department of Aerospace and Mechanical Engineering, Fitzpatrick Hall of Engineering and Cushing Hall, 112 N Notre Dame Ave, Notre Dame, IN 46556
2.
Human Research Engineering Directorate, CCDC Army Research Laboratory, 7101 Mulberry Point Road, Bldg. 459, Aberdeen Proving Ground, MD 21005-5425
3.
Department of Mathematics, University of Tennessee, 1403 Circle Dr., Knoxville, TN 37996

^* Corresponding authors: Christopher Oballe and Vasileios Maroulas
^* Corresponding authors: Christopher Oballe and Vasileios Maroulas

Received: December 2020

Revised: July 2021

Early access: September 2021

Published: December 2022

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Abstract

We propose ToFU, a new trainable neural network unit with a persistence diagram dissimilarity function as its activation. Since persistence diagrams are topological summaries of structures, this new activation measures and learns the topology of data to leverage it in machine learning tasks. We showcase the utility of ToFU in two experiments: one involving the classification of discrete-time autoregressive signals, and another involving a variational autoencoder. In the former, ToFU yields competitive results with networks that use spectral features while outperforming CNN architectures. In the latter, ToFU produces topologically-interpretable latent space representations of inputs without sacrificing reconstruction fidelity.

Keywords:

Mathematics Subject Classification: Primary: 55N31, 68T07.

Citation:

Full Text(HTML)

Figure 1. Here, we show a lower star filtration of image data. We model the image as a function $ f $ on a cubical complex $ \mathcal{K} $ whose constituent elementary cubes correspond to pixels. The value $ f(Q) $ of a pixel $ Q $ is its intensity. At the beginning of the filtration, $ \mathcal{K}_0 $, there are no cubes present. The four cubes with the lowest intensity appear in $ \mathcal{K}_1 $. During this time in the filtration, there is also a $ 1 $-cycle directly in the center of the image. Since this $ 1 $-cycle is not the boundary of any $ 2 $-chains, it corresponds to a $ 1 $-dimensional homological feature. More cubes are added in $ \mathcal{K}_2 $ and $ \mathcal{K}_3 $. Finally, the last cube is added at $ \mathcal{K}_4 $ and the $ 1 $-dimensional feature that appeared at $ \mathcal{K}_1 $ is annihilated

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Figure 2. The minimal cost matching (Equation (10)) for two PDs, $ \mathcal{D} $ and $ \mathcal{D}' $

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Figure 3. One-point PDs learned by ToFU for classification. The learned diagrams are color coded by accuracy. Example PDs from both classes in the classification task are also shown. Class 1 consisted of two types of PDs, depicted as upright and inverted triangles, respectively. Learned PDs corresponding to high classification accuracy fell into two groups– those with birth times earlier and later than points in Class 1 and Class 2, respectively

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Figure 4. Gradient descent with ToFU layer using Equation (15). Because the gradient depends on a minimal$ - $cost matching, initialization of weights determines the solution when there are fewer learnable points than those in data

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Figure 5. Gradient descent with ToFU layer that has two learnable points. Different initializations are shown in (a) and (b)

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Figure 6. Gradient descent to minimize the average of Equation (10) for two PDs using a ToFU layer that has three learnable points. Different initializations are shown in (a) and (b)

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Figure 7. A schematic of the encoder in our topological VAE architecture. Here, we choose C = 1 in $ \phi $ for simplicity

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Figure 8. Examples from the AR signals dataset. Here, we show signals with damping factor $ \beta $ = 4. The average log PSD for each class, estimated by averaging periodograms, is shown in the second column

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Figure 9. Shown above is a single example from each of the six classes in our synthetic dataset. We apply a nonlinear transformation to pixel values for visual clarity

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Figure 10. Latent space representations of the (a) typical VAE and (b) ToFU-VAE. The ToFU-VAE latent space shows clear separation based on the topology of each class

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Figure 11. A cubical complex, $ \mathcal{K} $ that we use to demonstrate homological computations. The $ 0 $-, $ 1 $-, and $ 2 $-dimensional cubes in $ \mathcal{K} $ are labelled as $ \{v_1, v_2, v_3, v_4, v_5, v_6\}, \{e_1, e_2, e_3, e_4, e_5, e_6, e_7\}, $ and $ \{f_1\} $, respectively

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Table 1. The ANN architecture we used for classification of simulated PDs

Layer	Description
1	ToFU. 1 unit. 1 learnable point.
2	Dense. 32 units. ReLU activations.
3	Dense. 16 units. ReLU activations.
4	Dense. 8 units. ReLU activations.
5	Dense. 1 unit. Sigmoid activation.

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Table 2. Test accuracies for each ANN trained for AR signal classification. Conv1 and Conv2 refer to the 8 and 64 channel networks, respectively, while PLs and PIs refer to the networks trained on persistence landscapes and persistence images

Model	Test Accuracy
Welch	98.91
ToFU	98.12
PLs	96.41
PIs	95.94
Conv1	92.66
Conv2	88.12

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Table 3. Test reconstruction errors for both VAEs

ANN	Test Recon. Err.
Typical VAE	0.0847
ToFU-VAE	0.0806

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