  In this work, we propose a method to efficiently compute label posteriors of
a base flat classifier in the presence of few validation examples within a
bottom-up hierarchical inference framework. A stand-alone validation set (not
used to train the base classifier) is preferred for posterior estimation to
avoid overfitting the base classifier, however a small validation set limits
the number of features one can effectively use. We propose a simple, yet
robust, logit vector compression approach based on generalized logits and label
confusions for the task of label posterior estimation within the context of
hierarchical classification. Extensive comparative experiments with other
compression techniques are provided across multiple sized validation sets, and
a comparison with related hierarchical classification approaches is also
conducted. The proposed approach mitigates the problem of not having enough
validation examples for reliable posterior estimation while maintaining strong
hierarchical classification performance.

Machine Learning;Computer Vision and Pattern Recognition