Efficient feature extraction with simultaneous recurrent network for metric learning

Abstract

Metric learning has been successful in distance based classification tasks. However, metric learning tends to become increasingly complex with the increase of input feature dimensionality. Therefore, application of an efficient feature extraction and dimensionality reduction technique prior to metric learning has been pursued. Conventional feature extraction and dimensionality reduction techniques used for metric learning are usually hand-crafted and may not offer the best overall performance. Contemporary methods such as deep neural networks (DNNs) along with metric learning have been used for improved feature extraction and dimensionality reduction through learning. While DNNs have exhibited excellent performance, such deep structures tend to get cumbersome with increasing complexity of the task. Consequently, this work attempts to introduce an efficient feature extraction and dimensionality reduction technique using a simultaneous recurrent network (SRN) architecture. The proposed SRN architecture with metric learning is tested on solving two complex classification tasks: facial expression and character recognition. Our results show that the proposed SRN feature extraction and metric learning classification pipeline achieves superior performance in comparison to a DNN-based feature reduction and metric learning pipeline. We also demonstrate that the proposed SRN manages to utilize far less trainable parameters than the comparable DNN model such as stacked autoencoders (SAEs) for the same set of tasks.