BUILT IN - ARTICLE INTRO SECOND COMPONENT
Geodesic distance matrices can reveal shape properties that are largely invariant to non-rigid deformations, and thus are often used to analyze and represent 3-D shapes. However, these matrices grow quadratically with the number of points. Thus for large point sets it is common to use a low-rank approximation to the distance matrix, which fits in memory and can be efficiently analyzed using methods such as multidimensional scaling (MDS). In this paper we present a novel sparse method for efficiently representing geodesic distance matrices using biharmonic interpolation. This method exploits knowledge of the data manifold to learn a sparse interpolation operator that approximates distances using a subset of points. We show that our method is 2x faster and uses 20x less memory than current leading methods for solving MDS on large point sets, with similar quality. This enables analyses of large point sets that were previously infeasible.
Authors
Alexander Huth
Related Content
Motion Segmentation by Exploiting Complementary Geometric Models
Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using...
WRPN: Wide Reduced-Precision Networks
For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in...
HeNet: A Deep Learning Approach on IntelR Processor...
This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep...
Learning Visual Knowledge Memory Networks for Visual Question...
Visual question answering (VQA) requires joint comprehension of images and natural language questions, where many questions can't be directly or...
