Forward Stepwise Deep Autoencoder-based Monotone Nonlinear Dimensionality Reduction Methods.

ABSTRACT: Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset. Principal components analysis (PCA) is a linear dimensionality reduction method in the sense that each principal component is a linear combination of the input variables. To allow features that are nonlinear functions of the input variables, many nonlinear dimensionality reduction methods have been proposed. In this paper we propose novel nonlinear dimensionality reduction methods based on bottleneck deep autoencoders (Kramer, 1991). Our contributions are two-fold: (1) We introduce a monotonicity constraint into bottleneck deep autoencoders for estimating a single nonlinear component and propose two methods for fitting the model. (2

SUBMITTER: Fong Y 

PROVIDER: S-EPMC8673912 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature