Knowledge Driven Dimension Reduction for Clustering

Davidson, Ian

Knowledge Driven Dimension Reduction for Clustering

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Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1034-1038.

Abstract

As A.I. algorithms are applied to more complex domains that involve high dimensional data sets there is a need to more saliently represent the data. However, most dimension reduction approaches are driven by objective functions that may not or only partially suit the end users requirements. In this work, we show how to incorporate general-purpose domain expertise encoded as a graph into dimension reduction in way that lends itself to an elegant generalized eigenvalue problem. We call our approach Graph-Driven Constrained Dimension Reduction via Linear Projection (GCDR-LP) and show that it has several desirable properties.Ian Davidson

Ian Davidson. 2009. Knowledge Driven Dimension Reduction for Clustering. In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09. IJCAI, 1034–1038. https://ijcai.org/proceedings/2009/175

@inproceedings{ijcai2009p175,
  title     = {Knowledge Driven Dimension Reduction for Clustering},
  author    = {Davidson, Ian},
  booktitle = {Proceedings of the Twenty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-09}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Hiroaki Kitano},
  pages     = {1034--1038},
  year      = {2009},
  url       = {https://ijcai.org/proceedings/2009/175},
}

TY  - CPAPER
TI  - Knowledge Driven Dimension Reduction for Clustering
AU  - Davidson, Ian
T2  - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2009
SP  - 1034
EP  - 1038
UR  - https://ijcai.org/proceedings/2009/175
ER  -