Graph is a popular data representation capturing relations among samples, such as images and documents. Many successful graph-based techniques, such as Regularized Laplacian and Random Walk, have been used for multimedia applications in retrieval and classification. In this talk, I will review a few novel graph-based techniques designed specifically to handle the new challenges associated with large-scale noisy multimedia data encountered on the Web. I will review (1) label diagnosis and spectral filtering techniques for removing unreliable labels, (2) anchor graph methods for scaling up graph-based techniques to gigantic data sets, and (3) multi-edge graph that captures heterogeneous similarities among multimedia data. Applications in Web multimedia retrieval and novel systems searching images with Brain Machine Interfaces will be presented.
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