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SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
chandrajit bajaj · Tingran Gao · Zihang He · Qixing Huang · Zhenxiao Liang

Fri Jul 13 07:20 AM -- 07:30 AM (PDT) @ K11

We introduce a principled approach for \emph{simultaneous mapping and clustering} (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 on the exactness of SMAC under established noise models. We also demonstrate the usefulness of the approach on synthetic and real datasets.

Author Information

cbajaj bajaj (University of Texas at Austin)
Tingran Gao (University of Chicago)
Zihang He (Tsinghua University)
Qixing Huang (The University of Texas at Austin)
Zhenxiao Liang (Tsinghua University)

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