Recent work has been done by Rajat Raina and other researchers at Stanford in applying sparse coding techniques to various classification problems. In this project we follow that tradition by applying sparse coding to the problem of classifying fMRI images. In particular, we try to classify fMRI images based on what the subject was doing when the fMRI image was obtained. The two classification ...

Sparse coding is a method for nding a representation of data in which each of the components of the representation is only rarely signiicantly active. Such a representation is closely related to redundancy reduction and independent component analysis, and has some neurophysiological plausibility. In this paper, we show how sparse coding can be used for denoising. Using maximum likelihood estima...

Recent research has shown the effectiveness of using sparse coding(Sc) to solve many computer vision problems. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which may reduce the feature quantization error and boost the sparse coding performance, we propose Kernel Sparse Representation(KSR). KSR is essentially the sparse coding technique in a high dime...

Sparse coding is a method for nding a representation of data in which each of the components of the representation is only rarely signiicantly active. Such a representation is closely related to the techniques of independent component analysis and blind source separation. In this paper, we investigate the application of sparse coding for image feature extraction. We show how sparse coding can b...