NEIGHBORHOOD FUNCTION DESIGN FOR EMBEDDING IN REDUCED DIMENSION

Jiun-Wei Liou, Cheng-Yuan Liou

2011

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

  1. Chang, H. and Yeung, D.-Y. (2006). Robust locally linear embedding. Pattern Recognition, 39:1053-1065.
  2. Daza-Santacoloma, G., Acosta-Medina, C. D., and G., C.- D. (2010). Regularization parameter choice in locally linear embedding. Neurocomputing, 73:1595-1605.
  3. Pan, Y., Ge, S. S., and Mamun, A. A. (2009). Weighted locally linear embedding for dimension reduction. Pattern Recognition, 42:798-811.
  4. Roweis, S. T. and Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326.
  5. Wei, L., Zeng, W., and Wang, H. (2010). K-means clustering with manifold. In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, pages 2095-2099. IEEE Xplore Digital Library and EI Compendex.
  6. Wen, G., Jiang, L., and Wen, J. (2009). Local relative transformation with application to isometric embedding. Pattern Recognition Letters, 30:203-211.
  7. Wen, G., Jiang, L., Wen, J., and Shadbolt, N. R. (2006). Clustering-based nonlinear dimensionality reduction on manifold. In PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence, pages 444-453. Springer-Verlag.
  8. Yeh, T., Chen, T.-Y., Chen, Y.-C., and Shih, W.-K. (2010). Efficient parallel algorithm for nonlinear dimensionality reduction on gpu. In 2010 IEEE International Conference on Granular Computing, pages 592-597. IEEE Computer Society.
  9. Zuo, W., Zhang, D., and Wang, K. (2008). On kernel difference-weighted k-nearest neighbor classification. Pattern Analysis and Applications, 11:247-257.
Download


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