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Non-parametric Uni-modality Constraints for Deep Ordinal Classification by Belharbi, Soufiane; Ayed, Ismail Ben; McCaffrey, Luke; Granger, Eric is a scholarly article available to read on EtoBox.

What is Non-parametric Uni-modality Constraints for Deep Ordinal Classification about?

We propose a new constrained-optimization formulation for deep ordinal classification, in which uni-modality of the label distribution is enforced implicitly via a set of inequality constraints over all the pairs of adjacent labels. Based on (c-1) constraints for c labels, our model is non-parametric and, therefore, more flexible than the existing deep ordinal classification techniques. Unlike these, it does not restrict the learned representation to a single and specific parametric model (or penalty) imposed on all the labels. Therefore, it enables the training to explore larger spaces of solutions, while removing the need for ad hoc choices and scaling up to large numbers of labels. It can be used in conjunction with any standard classification loss and any deep architecture. To tackle the ensuing challenging optimization problem, we solve a sequence of unconstrained losses based on a powerful extension of the log-barrier method. This handles effectively competing constraints and accommodates standard SGD for deep networks, while avoiding computationally expensive Lagrangian dual steps and outperforming substantially penalty methods. Furthermore, we propose a new performance metr

Author

Belharbi, Soufiane; Ayed, Ismail Ben; McCaffrey, Luke; Granger, Eric

Published

2019

Language

EN