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Learning Marginalization through Regression for Hand Orientation Inference

Asad, M. and Slabaugh, G.G. (2016). Learning Marginalization through Regression for Hand Orientation Inference. Paper presented at the The 2nd Workshop on Observing and Understanding Hands in Action, 1 Jul 2016, Las Vegas, USA.


We present a novel marginalization method for multilayered Random Forest based hand orientation regression. The proposed model is composed of two layers, where the first layer consists of a marginalization weights regressor while the second layer contains expert regressors trained on subsets of our hand orientation dataset. We use a latent variable space to divide our dataset into subsets. Each expert regressor gives a posterior probability for assigning a given latent variable to the input data. Our main contribution comes from the regression based marginalization of these posterior probabilities. We use a Kullback-Leibler divergence based optimization for estimating the weights that are used to train our marginalization weights regressor. In comparison to the state-of-the-art of both hand orientation inference and multi-layered Random Forest marginalization, our proposed method proves to be more robust.

Publication Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Mathematics, Computer Science & Engineering > Engineering
Text - Accepted Version
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