  Random Projection (RP) technique has been widely applied in many scenarios
because it can reduce high-dimensional features into low-dimensional space
within short time and meet the need of real-time analysis of massive data.
There is an urgent need of dimensionality reduction with fast increase of big
genomics data. However, the performance of RP is usually lower. We attempt to
improve classification accuracy of RP through combining other reduction
dimension methods such as Principle Component Analysis (PCA), Linear
Discriminant Analysis (LDA), and Feature Selection (FS). We compared
classification accuracy and running time of different combination methods on
three microarray datasets and a simulation dataset. Experimental results show a
remarkable improvement of 14.77% in classification accuracy of FS followed by
RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield
a more discriminative subspace with an increase of 13.65% on classification
accuracy on the same dataset. FS followed by RP outperforms other combination
methods in classification accuracy on most of the datasets.
