Abstract
Ordinal regression is a supervised learning problem
aiming to classify instances into ordinal categories. It is
challenging to automatically extract high-level features for
representing intraclass information and interclass ordinal
relationship simultaneously. This paper proposes a constrained optimization formulation for the ordinal regression problem which minimizes the negative loglikelihood
for multiple categories constrained by the order relationship between instances. Mathematically, it is equivalent to
an unconstrained formulation with a pairwise regularizer.
An implementation based on the CNN framework is proposed to solve the problem such that high-level features can
be extracted automatically, and the optimal solution can be
learned through the traditional back-propagation method.
The proposed pairwise constraints make the algorithm work
even on small datasets, and a proposed efficient implementation make it be scalable for large datasets. Experimental
results on four real-world benchmarks demonstrate that the
proposed algorithm outperforms the traditional deep learning approaches and other state-of-the-art approaches based
on hand-crafted features