Deep Spectral Ranking

ILKAY YILDIZ POTTER, Jennifer Dy, Deniz Erdogmus, Susan Ostmo, J. Peter Campbell, Michael Chiang, Stratis Ioannidis

Abstract:

Learning from ranking observations arises in
many domains, and siamese deep neural networks have shown excellent inference performance in this setting. However, SGD does not
scale well, as an epoch grows exponentially
with the ranking observation size. We show
that a spectral algorithm can be combined
with deep learning methods to significantly
accelerate training. We combine a spectral
estimate of Plackett-Luce ranking scores with
a deep model via the Alternating Directions
Method of Multipliers with a Kullback-Leibler
proximal penalty. Compared to a state-ofthe-art siamese network, our algorithms are
up to 175 times faster and attain better predictions by up to 26% Top-1 Accuracy and
6% Kendall-Tau correlation over five real-life
ranking datasets.