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Compressing Sparse Feature Vectors Using Random Ortho-Projections
2010 20th International Conference on Pattern Recognition
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in classification tasks instead of concentrating on reconstruction accuracy. In the random ortho-projection method, the mapping for the compression can be obtained without any further knowledge of the original features. This makes the approach favorable if training data is costly or impossible to obtain. The independencedoi:10.1109/icpr.2010.345 dblp:conf/icpr/RahtuSH10 fatcat:u2mwzdxhrba6nbucobfwprt3da