# NEIGHBORHOOD FUNCTION DESIGN FOR EMBEDDING IN REDUCED DIMENSION

### Jiun-Wei Liou, Cheng-Yuan Liou

#### Abstract

LLE(Local linear embedding) is a widely used approach for dimension reduction. The neighborhood selection is an important issue for LLE. In this paper, the e-distance approach and a slightly modified version of k-nn method are introduced. For different types of datasets, different approaches are needed in order to enjoy higher chance to obtain better representation. For some datasets with complex structure, the proposed Ɛ-distance approach can obtain better representations. Different neighborhood selection approaches will be compared by applying them to different kinds of datasets.

#### References

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#### Paper Citation

#### in Harvard Style

Liou J. and Liou C. (2011). **NEIGHBORHOOD FUNCTION DESIGN FOR EMBEDDING IN REDUCED DIMENSION** . In *Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)* ISBN 978-989-8425-84-3, pages 190-195. DOI: 10.5220/0003681201900195

#### in Bibtex Style

@conference{ncta11,

author={Jiun-Wei Liou and Cheng-Yuan Liou},

title={NEIGHBORHOOD FUNCTION DESIGN FOR EMBEDDING IN REDUCED DIMENSION},

booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},

year={2011},

pages={190-195},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0003681201900195},

isbn={978-989-8425-84-3},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)

TI - NEIGHBORHOOD FUNCTION DESIGN FOR EMBEDDING IN REDUCED DIMENSION

SN - 978-989-8425-84-3

AU - Liou J.

AU - Liou C.

PY - 2011

SP - 190

EP - 195

DO - 10.5220/0003681201900195