Pattern recognition via projection-based kNN rules

Ricardo Fraiman, Ana Justel and Marcela Svarc

Computational Statistics & Data Analysis, 2010, vol. 54, issue 5, 1390-1403

Abstract: A new procedure for pattern recognition is introduced based on the concepts of random projections and nearest neighbors. It can be considered as an improvement of the classical nearest neighbor classification rules. Besides the concept of neighbors, the notion of district, a larger set into which the data will be projected, is introduced. Then a one-dimensional kNN method is applied to the projected data on randomly selected directions. This method, which is more accurate to handle high-dimensional data, has some robustness properties. The procedure is also universally consistent. Moreover, the method is challenged with the Isolet data set where a very high classification score is obtained.

Keywords: High-dimensional; data; kNN; rules; Pattern; recognition; Robustness (search for similar items in EconPapers)
Date: 2010
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