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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chunyu Liu Young-Min Jang Ozawa, S. Minho Lee |
| Copyright Year | 2011 |
| Description | Author affiliation: School of Electrical Engineering and Computer Science, Kyungpook National University, Taegu 702-701, Korea (Young-Min Jang; Minho Lee) || Graduate School of Engineering, Kobe University, 657-8501, Japan (Chunyu Liu; Ozawa, S.) |
| Abstract | In this paper, we propose an incremental 2-directional 2-dimensional linear discriminant analysis $(I-(2D)^{2}LDA)$ for multitask pattern recognition (MTPR) problems in which a chunk of training data for a particular task are given sequentially and the task is switched to another related task one after another. In $I-(2D)^{2}LDA,$ a discriminant space of the current task spanned by 2 types of discriminant vectors is augmented with effective discriminant vectors that are selected from other tasks based on the class separability. We call the selective augmentation of discriminant vectors knowledge transfer of feature space. In the experiments, the proposed $I-(2D)^{2}LDA$ is evaluated for the three tasks using the ORL face data set: person identification (Task 1), gender recognition (Task 2), and young-senior discrimination (Task 3). The results show that the knowledge transfer works well for Tasks 2 and 3; that is, the test performance of gender recognition and that of young-senior discrimination are enhanced. |
| Starting Page | 2911 |
| Ending Page | 2916 |
| File Size | 326046 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033603 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training data Face Knowledge transfer Pattern recognition Training Accuracy Feature extraction |
| Content Type | Text |
| Resource Type | Article |
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