Work

Sketched Clustering via Hybrid Approximate Message Passing

Year: 2017

Type: conference-paper

Abstract: In sketched clustering, a dataset of T samples is first sketched down to a vector of modest size, from which the centroids are subsequently extracted. Its advantages include 1) reduced storage complexity and 2) centroid extraction complexity independent of T. For the sketching methodology recently proposed by Keriven et al., which can be interpreted as a random sampling of the empirical characteristic function, we propose a sketched clustering algorithm based on approximate message passing. Numerical experiments suggest that our approach is more efficient than the state-of-the-art sketched clustering algorithm “CL-OMPR” (in both computational and sample complexity) and more efficient than k-means++ when T is large.

Source: 2018 52nd Asilomar Conference on Signals, Systems, and Computers

DOI: 10.1109/acssc.2017.8335370

Authors: Evan Byrne, Antoine Chatalic, Remi Gribonval, Philip Schniter

Institutions: The Ohio State University, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Institut de Recherche en Informatique et Systèmes Aléatoires, Université de Rennes

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Ohio State University, Lincoln Laboratory, Massachusetts Institute of Technology

Award: Approximate Message Passing Algorithms and Networks