This report explores the implementation of Variational Autoencoders (VAEs) for dimensionality reduction in multivariate statistics. We benchmark VAEs against traditional methods such as PCA, ICA, and KPCA, using datasets including Iris Flowers, Wine, and Breast Cancer. The study further extends the application of VAEs to non-columnar data, specifically medical heartbeat audio recordings. The results demonstrate the strengths and limitations of VAEs in clustering, manifold learning, and anomaly detection, particularly in the context of medical data. Our findings suggest that while VAEs offer flexible non-linear representations, their performance is highly sensitive to the type of activation function used and the nature of the data.
Benchmarking Variational Autoencoders for Dimensionality Reduction and Application to Medical Heartbeats Data
2024-11-04
820478 byte
Conference paper
Electronic Resource
English
Variational Autoencoders for Dimensionality Reduction of Automotive Vibroacoustic Models
British Library Conference Proceedings | 2022
|Variational Autoencoders for Dimensionality Reduction of Automotive Vibroacoustic Models
SAE Technical Papers | 2022
|Springer Verlag | 2022
|Deep Tracking Portfolios Using Autoencoders and Variational Autoencoders
Springer Verlag | 2024
|