Principal Component Analysis, PCA, F-transform, image classification, algorithm complexity

While machine learning algorithms become more and more accurate in image processing tasks, their computation complexity becomes less important because they can be run on more and more powerful hardware. In this work, we are considering the computation complexity of a machine learning algorithm training/classification phase as the major criterion. The main aim is given to the Principal Component Analysis algorithm, which is examined, its drawbacks are point-out and suppressed by the proposed combination with the F-transform technique. We show that the training phase of such a combination is very fast, which is caused by the fact that both PCA and F-transform algorithms reduce dimensionality. In the designed benchmark, we show that the success rate of the fast hybrid algorithm is the same as the original PCA, due to F-transform ability to capture spatial information and reduction of noise/distortion in an image. Finally, we demonstrate that PCA+FT is faster and can achieve a higher success rate than a standard Convolution Neural Network and nevertheless, it is slightly less accurate as a Capsule Neural Network for the chosen dataset, its training phase is 100000x faster and classification time is faster 9x.