Not all signals are created equal: Dynamic Objective Auto-Encoder for Multivariate Data

There is a representational capacity limit in a neural network defined by the number of hidden units. For multimodal time-series data, there could exist signals with various complexity and redundancy. One way of getting a higher representational capacity for such input data is to increase the number of units in the hidden layer. We propose a step towards dynamically change the number of units in the visible layer so that there is less focus on signals that are difficult to reconstruct and more focus on signals that are easier to reconstruct with the goal to improve classification accuracy and also better understand the data itself. A comparison with state-of-the-art architectures show that our model achieves a slightly better classification accuracy on the task of classifying various styles of human motion.