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Multiscale geometric dictionaries for point-cloud data.
| Content Provider | CiteSeerX |
|---|---|
| Author | Chen, Guangliang Maggioni, Mauro |
| Abstract | We develop a novel geometric multiresolution analysis for analyzing intrinsically low-dimensional point clouds in highdimensional spaces, modeled as samples from a d-dimensional set M (in particular, a manifold) embedded in R D, in the regime d ≪ D. This type of situation has been recognized as important in various applications, such as the analysis of sounds, images, and gene arrays. In this paper we construct data-dependent multiscale dictionaries that aim at efficient encoding and manipulating of the data. Unlike existing constructions, our construction is fast, and so are the algorithms that map data points to dictionary coefficients and vice versa. In addition, data points have a guaranteed sparsity in terms of the dictionary. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Multiscale Geometric Dictionary Point-cloud Data Data Point Gene Array Vice Versa Low-dimensional Point Cloud D-dimensional Set Novel Geometric Multiresolution Analysis Highdimensional Space Data-dependent Multiscale Dictionary Dictionary Coefficient Various Application |
| Content Type | Text |
| Resource Type | Article |
