Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPRW.2016.154 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
Field | DocType | Volume |
Pattern recognition,Regression,Inference,Computer science,Posterior probability,Social exclusion,Latent variable,Artificial intelligence,Random forest,Statistics | Conference | 2016 |
Issue | ISSN | Citations |
1 | 2160-7508 | 1 |
PageRank | References | Authors |
0.35 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Asad | 1 | 28 | 10.57 |
| Gregory G. Slabaugh | 2 | 870 | 71.13 |