Multilayer bootstrap networks

Abstract

Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear network from bottom up for unsupervised nonlinear dimensionality reduction. Each layer of the network is a nonparametric density estimator. It consists of a group of k-centroids clusterings. Each clustering randomly selects data points with randomly selected features as its centroids, and learns a one-hot encoder by one-nearest-neighbor optimization. Geometrically, the nonparametric density estimator at each layer projects the input data space to a uniformly-distributed discrete feature space, where the similarity of two data points in the discrete feature space is measured by the number of the nearest centroids they share in common. The multilayer network gradually reduces the nonlinear variations of data from bottom up by building a vast number of hierarchical trees implicitly on the original data space. Theoretically, the estimation error caused by the nonparametric density estimator is proportional to the correlation between the clusterings, both of which are reduced by the randomization steps.

accepted for publication by Neural Networks

References in corpus (1)

• A survey of dimensionality reduction techniques

Cited by in corpus (4)

• Multilayer bootstrap network for unsupervised speaker recognition
• Unsupervised model compression for multilayer bootstrap networks
• Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?
• An Investigation of Universal Background Sparse Coding Based Speaker Verification on TIMIT