Adaptive dimensionality reduction for neural network-based online principal component analysis.

ABSTRACT: "Principal Component Analysis" (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller set of linearly independent variables, the so-called principal components, which capture a large portion of the data variance. The problem of finding the optimal number of principal components has been widely studied for offline PCA. However, when working with streaming data, the optimal number changes continuously. This requires to update both the principal components and the dimensionality in every timestep. While the continuous update of the principal components is widely studied, the available algorithms for dimensionality adjustment are limited to an increment of one in neural network-bas

SUBMITTER: Migenda N 

PROVIDER: S-EPMC8009402 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature