Flexible and Discriminative Non-linear Embedding with Feature Selection for Image Classification
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Flexible and Discriminative Non-linear Embedding with Feature Selection for Image Classification |
| Type de publication | Conference Paper |
| Year of Publication | 2018 |
| Auteurs | Zhu R., Dornaika F., Ruichek Y. |
| Conference Name | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
| Publisher | Int Assoc Pattern Recognit; Chinese Assoc Automat |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-5386-3788-3 |
| Mots-clés | discriminative nonlinear embedding, Feature selection, semi-supervised learning, sparse regression |
| Résumé | In the past years, various graph-based data embedding algorithms were proposed and used in machine learning and pattern recognition fields. This paper introduces a graph-based non-linear embedding learning algorithm for image classification and recognition. The proposed embedding method can be used for supervised and semi-supervised learning settings. The proposed criterion allows the simultaneous estimation of a linear and a non-linear embedding. It integrates manifold smoothness, Sparse Regression and Margin Discriminant Embedding. The deployed sparse regression implicitly performs feature selection on the original features of the data matrix and of the linear transform. The proposed method is applied to four image datasets: 8 Sports Event Categories dataset, Scene 15 dataset, ORL Face dataset and COIL-20 Object dataset. The experiments demonstrate the effectiveness of the proposed embedding method. |