Recursive reduction net for large-scale high-dimensional data
Part Of
Proceedings - International Conference on Image Processing, ICIP
Start Page
1903
End Page
1907
ISBN (of the container)
978-146739961-6
Date Issued
2016-09
Author(s)
Tyng-Luh Liu
Abstract
Performing dimensionality reduction on features is essential in tackling a majority of large-scale computer vision and pattern recognition problems. The popularity of adopting high-dimensional descriptors has caused conventional techniques such as PCA inefficient or even unfeasible. We introduce an unsupervised deep-net approach, termed as recursive reduction net (RRN), to carrying out dimensionality reduction for large-scale high-dimensional data. The proposed iterative algorithm is designed to learn how to merge piecewise reduction results effectively. To this end, we use PCA as the teacher model to establish a reduction net and a fusion net, respectively. To demonstrate the usefulness of RRN, we evaluate the property of variance explaining and carry out extensive experiments on similarity search via binary coding, which would benefit from a proper dimensionality-reduction scheme.
SDGs
Publisher
IEEE
Type
conference paper