Multiscale manifold representation and modeling

Many real world data sets can be viewed as points in a higher-dimensional space that lie concentrated around a lower-dimensional manifold structure. We propose a new multiscale representation for such point clouds based on lifting and perfect matching. The result is an adaptive wavelet transform that decomposes a point cloud into manifold approximations and details at multiple scales. We illustrate with several examples that the transform can extract an unknown smooth manifold from noisy point cloud samples using simple wavelet thresholding ideas.