@article{
 title = {Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {10},
 websites = {http://arxiv.org/abs/1412.4098},
 month = {12},
 day = {12},
 id = {df92ce33-7d1f-3b0d-a461-82cb296300a4},
 created = {2015-06-19T08:32:46.000Z},
 accessed = {2015-06-19},
 file_attached = {false},
 profile_id = {182bbbf9-24a3-3af3-9ed6-563e8f89259b},
 group_id = {8d229673-0aec-3014-b0f6-eda47f83e147},
 last_modified = {2015-06-19T08:32:46.000Z},
 read = {false},
 starred = {true},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Shen2014a},
 genre = {Machine Learning},
 abstract = {In this paper we present a nonlinear manifold matching algorithm to match multiple data sets using shortest-path distance and joint neighborhood selection. This is effectively achieved by combining Isomap \citeTenenbaumSilvaLangford2000 and the matching methods from \citePriebeMarchette2012. Our approach exhibits superior and robust performance for matching data from disparate sources, compared to algorithms that do not use shortest-path distance or joint neighborhood selection.},
 bibtype = {article},
 author = {Shen, Cencheng and Priebe, Carey E.},
 journal = {arXiv preprint}
}

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