  Kernel Principal Component Analysis (KPCA) is a popular dimensionality
reduction technique with a wide range of applications. However, it suffers from
the problem of poor scalability. Various approximation methods have been
proposed in the past to overcome this problem. The Nystr\"om method, Randomized
Nonlinear Component Analysis (RNCA) and Streaming Kernel Principal Component
Analysis (SKPCA) were proposed to deal with the scalability issue of KPCA.
Despite having theoretical guarantees, their performance in real world learning
tasks have not been explored previously. In this work the evaluation of SKPCA,
RNCA and Nystr\"om method for the task of classification is done for several
real world datasets. The results obtained indicate that SKPCA based features
gave much better classification accuracy when compared to the other methods for
a very large dataset.
