title: Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis.
abstract: Matching one set of objects to another is a ubiquitous task in machine learning and computer vision that often reduces to some form of the quadratic assignment problem (QAP). The QAP is known to be notoriously hard, both in theory and in practice. Here, we investigate if this difficulty can be mitigated when some additional piece of information is available: (a) that all QAP instances of interest come from the same application, and (b) the correct solution for a set of such QAP instances is given. We propose a new approach to accelerate the solution of QAPs based onlearningparameters for a modified objective function from prior QAP instances. A key feature of our approach is that it takes advantage of the algebraic structure of permutations, in conjunction with special methods for optimizing functions over the symmetric group 𝕊nin Fourier space. Experiments show that in practical domains the new method can outperform existing approaches.
authors.affiliation: Dept. of Computer Sciences, University of Wisconsin Madison.
authors.forename: Deepti
authors.initials: D
authors.lastname: Pachauri
authors.affiliation: Dept. of Biostatistics & Med. Informatics, University of Wisconsin Madison.
authors.forename: Maxwell
authors.initials: M
authors.lastname: Collins
authors.affiliation: Dept. of Computer Science and Dept. of Statistics, University of Chicago.
authors.forename: Risi
authors.initials: R
authors.lastname: Kondor
authors.forename: Vikas
authors.initials: V
authors.lastname: Singh