SOM-VAE: Interpretable Discrete Representation Learning on Time Series

By Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch. 2019

155 citations recorded by the source corpus.

High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time. To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space. This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty. We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Generative Models, Representation & Self-Supervised Learning, Signal Processing

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Citations in the corpus

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020, background): “There is for example a rich literature in disentangling pose from content in 3D objects and content from motion in videos or time series in general (Yang et al., 2015; Yingzhen and Mandt, 2018a; Hsieh et al., 2018; Fortuin et al., 2019; Deng et al., 2017; Goroshin et al., 2015).”
On the Fairness of Disentangled Representations (2019, background): “Disentangling pose from content and content from motion are also classical computer vision problems that have been tackled with various degrees of supervision and inductive bias [92, 93, 40, 25, 18, 31, 42].”
Disentangling Factors of Variation Using Few Labels (2019, background): “[15] Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, and Gunnar Rätsch.”
A Transformer-based Framework for Multivariate Time Series Representation Learning (2020, background): “As interesting variations of the former, Kopf et al. (2019) and Fortuin et al. (2019) additionally incorporated Variational Autoencoding into this approach, but focused on clustering and the visualization of shifting sample topology with time.”
Quo vadis artificial intelligence? (2022, background): “As for question (ii), temporal logic-based methods are promising solutions [136].”
GP-VAE: Deep Probabilistic Time Series Imputation (2019, background): “Conversely, there are deep probabilistic models for time series [e.g., 14, 26, 27], but those do not explicitly handle missing data.”
NCP-VAE: Variational Autoencoders with Noise Contrastive Priors (2020, background): “…2020), speech generation (Oord et al., 2016; Ping et al., 2020), image captioning (Aneja et al., 2019; Deshpande et al., 2019; Aneja et al., 2018), semi-supervised learning (Kingma et al., 2014; Izmailov et al., 2020), and representation learning (Van Den Oord et al., 2017; Fortuin et al., 2018).”