A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, ...
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapir...
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph with the input data points as...
A histogram-based method for the interpretation of three-dimensional (3D) point clouds is introduced, where point clouds represent the surface of a scene of multiple objects and ba...
A number of problems are well suited for volumetric representation for both simulation and storage, however, the large amount of data that needs to be processed and rendered with ...
Shigeru Muraki, Eric B. Lum, Kwan-Liu Ma, Masato O...
Distance functions are an important component in many learning applications. However, the correct function is context dependent, therefore it is advantageous to learn a distance f...