Work

SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions.

Year: 2018

Type: article

Abstract: (SMAC) for establishing consistent maps across heterogeneous object collections (e.g., 2D images or 3D shapes). Our approach takes as input a heterogeneous object collection and a set of maps computed between some pairs of objects, and outputs a homogeneous object clustering together with a new set of maps possessing optimal intra- and inter-cluster consistency. Our approach is based on the spectral decomposition of a data matrix storing all pairwise maps in its blocks. We additionally provide tight theoretical guarantees for the accuracy of SMAC under established noise models. We also demonstrate the usefulness of our approach on synthetic and real datasets.

Source: PubMed

Authors: Chandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang

Institutions: The University of Texas at Austin, University of Chicago, Tsinghua University

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Institute of General Medical Sciences

Award: Mathematical Chemical Imaging with Uncertainty Quantification

OpenAlex ID: W2804286000

FWCI: 1.452

Cites: 40

Cited by: 16

Related to: 10