Sparse canonical correlation analysis for recognition
Canonical correlation analysis (CCA) is one promising feature extraction and subspace learning method for multivariate vectors by exploiting the correlation between two multidimensional variables in a linear way. Hence CCA has been widely employed in many applications such as statistics, economics and signal processing. However, the traditional CCA may be difficult to interpret especially when the original variables are expected to involve only a few components. In this paper, we propose sparse canonical correlation analysis (SCCA) to overcome the above problem. SCCA can find a reasonable trade-off between statistical fidelity and interpretability. Furthermore, we use a generalized power method to optimize the proposed SCCA algorithm. And finally we conduct extensive experiments for recognition on several popular databases including UCI datasets and USAA dataset. Experimental results demonstrate that the proposed SCCA algorithm outperforms the traditional CCA algorithm.
Sparse Principle Motion Component for One-shot Gesture Recognition
With the rapid development of computer vision technology, gesture recognition has attracted much attention in recent years. However, the traditional gesture recognition methods waste a lot of time in the process of building a model with a large number of examples. To tackle the above problems, in this paper we propose sparse PCA based principle motion component (SPMC) method for one-shot gesture recognition, which can properly enhance recognition accuracy only with few training examples and unspecialized sensors. To evaluate the SPMC method, we conduct one-shot gesture recognition experiments on ChaLearn Gesture Dataset. Experimental results show that the proposed approach can improve the accuracy of gesture recognition.