Key result
VAEs provide flexible nonlinear representations for heartbeat data but remain highly sensitive to activation functions.
VAEs can be applied to medical heartbeat audio recordings for dimensionality reduction, but their performance is sensitive to activation functions and data types.
VAE sensitivity to activation functions precludes routine clinical use for heartbeat data; leaves open optimized nonlinear modeling in cardiovascular research.
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 nonlinear representations, their performance is highly sensitive to the type of activation function used and the nature of the data.
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Irshad A Buchh (2024) studied Medical heartbeat audio recordings. Variational Autoencoders (VAEs) vs. PCA, ICA, and KPCA was evaluated on Dimensionality reduction, clustering, manifold learning, and anomaly detection. Variational Autoencoders provided flexible nonlinear representations for medical heartbeat data, though performance was highly sensitive to the activation function and data nature.
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