Deep Kernel Principal Component Analysis for Multi-level Feature Learning

Understand

Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction.

• Modern deep learning tools have achieved great empirical success, but a framework for deep principal component analysis is still lacking.
• Here we develop a deep kernel PCA methodology (DKPCA) to extract multiple levels of the most informative components of the data.
• Our scheme can effectively identify new hierarchical variables, called deep principal components, capturing the main characteristics of high-dimensional data through a simple and interpretable numerical optimization.