Collective Kernel Construction in Noisy Environment

Kernels are similarity functions, and play important roles in machine learning. Traditional kernels are built directly from the feature vectors of data instances xi, xj . However, data could be noisy, and there are missing values or corrupted values in feature vectors. In this paper, we propose a new approach to build kernel Collective Kernel, especially from noisy data. We also derive an efficient algorithm to solve the L1-norm based optimization. Extensive experiments on face data, hand written characters and image scene datasets show improved performance for clustering and semi-supervised classification tasks on our collective kernel comparing with the traditional gaussian kernel.