Authors:
Jordan Gonzalez
1
;
Thibault Geoffroy
1
;
Aurelia Deshayes
2
and
Lionel Prevost
1
Affiliations:
1
Learning, Data and Robotics (LDR) Lab, ESIEA, Paris, France
;
2
Laboratoire d’Analyse et Mathématiques Appliquées (LAMA), UPEC, Créteil, France
Keyword(s):
Incremental Learning, Semi-Supervised Learning, Co-Training, Random Forest, Emotion Recognition.
Abstract:
In this work, we propose to adapt a generic emotion recognizer to a set of individuals in order to improve its accuracy. As this adaptation is weakly supervised, we propose a hybrid framework, the so-called co-incremental learning that combines semi-supervised co-training and incremental learning. The classifier we use is a specific random forest whose internal nodes are nearest class mean classifiers. It has the ability to learn incrementally data covariate shift. We use it in a co-training process by combining multiple view of the data to handle unlabeled data and iteratively learn the model. We performed several personalization and provided a comparative study between these models and their influence on the co-incrementation process. Finally, an in-depth study of the behavior of the models before, during and after the co-incrementation process was carried out. The results, presented on a benchmark dataset, show this hybrid process increases the robustness of the model, with only a
few labeled data.
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