bi-functional autoencoder

Assembly Line

Making space for the data economy - A machine learning solution for data compression

📅 Date:

✍️ Author: Aniruddha Rajendra

🔖 Topics: bi-functional autoencoder

🏢 Organizations: Hitachi

To overcome the limitations of existing methods, my colleagues and I at Hitachi America R&D have been investigating a novel approach using FDA and BFAE. BFAE offers a general functional mapping from multivariate temporal variables to themselves, preserving the functional nature of the data. This approach captures non-linear relationships while reducing dimensions in terms of both features and time points. By utilizing both a functional encoder and a functional decoder, our approach provides flexibility in transforming the data into low-dimensional latent representations. This flexibility allows for different analytical tasks such as prediction, classification, clustering, and forecasting.

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