Recursive reduction net for large-scale high-dimensional data
Tsung-Wei Ke, Tyng-Luh Liu · 2016
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.