Reducing High-Dimensional Data by Principal Component Analysis vs. Random Projection for Nearest Neighbor Classification

@article{Deegalla2006ReducingHD,
  title={Reducing High-Dimensional Data by Principal Component Analysis vs. Random Projection for Nearest Neighbor Classification},
  author={Sampath Deegalla and Henrik Bostr{\"o}m},
  journal={2006 5th International Conference on Machine Learning and Applications (ICMLA'06)},
  year={2006},
  pages={245-250}
}
The computational cost of using nearest neighbor classification often prevents the method from being applied in practice when dealing with high-dimensional data, such as images and micro arrays. One possible solution to this problem is to reduce the dimensionality of the data, ideally without loosing predictive performance. Two different dimensionality reduction methods, principle component analysis (PCA) and random projection (RP), are investigated for this purpose and compared w.r.t. the… CONTINUE READING
Highly Cited
This paper has 98 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 41 extracted citations

Postcapture Focusing Using Regression Forest

IEEE Signal Processing Letters • 2017
View 1 Excerpt

Compressed Signal Processing on Nyquist-Sampled Signals

IEEE Transactions on Computers • 2016
View 1 Excerpt

98 Citations

01020'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 98 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 17 references

Zubud - zurich building database for image based recognition

H.Shao, T. Svoboda, L. V. Gool
Technical report, Computer Vision Lab, Swiss Federal Institute of Technology, Switzerland, • 2003
View 2 Excerpts
Highly Influenced

Data Mining: Practical Machine Learning Tools and Techniques

SIGMOD Record • 1999
View 3 Excerpts
Highly Influenced

Content-based image retrieval in medical applications: a novel multistep approach

T. M. Lehmann, B. B. Wein, +3 authors M. Kohnen
M. M. Yeung, B.-L. Yeo, and C. A. Bouman, editors, Proceedings of SPIE: Storage and Retrieval for Media Databases 2000, volume 3972, pages 312–320 • 2000
View 1 Excerpt

Similar Papers

Loading similar papers…