Compressing Sparse Feature Vectors Using Random Ortho-Projections

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 independence from the data also enables one to embed the compression scheme directly into the computation of the original features. Our study is inspired by the results in compressive sensing, which state that up to a certain compression ratio and with high probability, such projections result in no loss of information. In comparison to learning based compression, namely principal component analysis (PCA), the random projections resulted in comparable performance already at high compression ratios depending on the sparsity of the original features.