NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Computational Statistics & Data Analysis
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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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.