Monitoring smartphones for anomaly detection
Mobile Networks and Applications
Classification of microarrays with kNN: comparison of dimensionality reduction methods
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Distributed high dimensional information theoretical image registration via random projections
Digital Signal Processing
Approximate document outlier detection using random spectral projection
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Approximate high-dimensional nearest neighbor queries using R-forests
Proceedings of the 17th International Database Engineering & Applications Symposium
DIMO: distributed index for matching multimedia objects using MapReduce
Proceedings of the 5th ACM Multimedia Systems Conference
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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 performance of the resulting nearest neighbor classifier on five image data sets and five micro array data sets. The experiment results demonstrate that PCA outperforms RP for all data sets used in this study. However, the experiments also show that PCA is more sensitive to the choice of the number of reduced dimensions. After reaching a peak, the accuracy degrades with the number of dimensions for PCA, while the accuracy for RP increases with the number of dimensions. The experiments also show that the use of PCA and RP may even outperform using the non-reduced feature set (in 9 respectively 6 cases out of 10), hence not only resulting in more efficient, but also more effective, nearest neighbor classification.