Linear Analysis Metric Learning using Siamese Neural Networks
Abin Jose, Qi Mei
Submitted to IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2022),
Conference homepage
Abstract
We propose a method for learning the Linear Discriminant Analysis (LDA) using a Siamese Neural Network (SNN) architecture. The novelty of our work is that we learn the LDA projection matrix between the final fully-connected layers of an SNN. An SNN architecture is used since the proposed loss maximizes the Kullback-Leibler divergence between the feature distributions from the two branches of an SNN. The network learns an optimized feature space having inherent properties pertaining to the learning of LDA. The learned image descriptors are a) low-dimensional, b) have small intra-class variance c) large inter-class variance, and d) is able to classify the classes with linear decision hyperplanes. The proposed method has the advantage that the LDA learning happens end-to-end. We measured the classification accuracy in three datasets such as MNIST, CIFAR-10, and STL-10 and compared the performance with other state-of-the-art methods. We also measured the KL divergence between the class pairs and visualized the projection of feature vectors along the learned discriminant directions.
Supplementary Material
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