Within-class subspace regularization for human activity recognition
| Autor: | Festus Osayamwen, Jules R. Tapamo |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Dimensionality reduction Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology Linear discriminant analysis Regularization (mathematics) Activity recognition Discriminative model Principal component analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Subspace topology Mathematics |
| Zdroj: | ICIT |
| DOI: | 10.1109/icit.2016.7474854 |
| Popis: | A subspace regularization approach is proposed for eigenfeatures extraction and regularization in human activity recognition. In this approach the within-class subspace is modelled using more eigenvalues from the reliable subspace to obtain a four parameter modelling scheme. This regularization is done in one piece, therefore avoiding undue complexity of modelling eigenspectrum differently. The whole space is used for performance evaluation because feature extraction and dimensionality reduction is done at later stage of the evaluation process. Results show that the proposed approach has better discriminative capacity than several other subspace approaches for human activity recognition. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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