Abstract:
The emergence of big data has increased the importance of dimensionality reduction
in every field related to data analysis. The search for an optimal and generalized dimensionality
reduction method is very important to reduce and optimize the time needed for
data execution. Dimensionality reduction is the most needed part in any type of data
pre-processing and visualization. The idea of this research study is basically to compare
deep learning methods (Auto-encoders) with principal component analysis (PCA) for dimensionality
reduction and to investigate about the performance of these methods based
on different datasets. Due to the lack of any general evaluation measure for dimensionality
reduction methods, we have tried to compare the methods based on the measure of
classification accuracy and the execution time of the models. For this purpose, a baseline
classification model has been developed for each dataset using logistic regression by
utilizing all the original information of the data. The baseline accuracy is then used as
an evaluation measure for comparing the methods performance. The methods have been
applied on two different types of real-world datasets: Human activity recognition system
(HAR) dataset and MNIST dataset. The results show that PCA performs better both in
terms of the classification accuracy and execution time on the HAR. Deep auto-encoder improved
the classification accuracy of the logistic model for MNIST data, but it was not able
to optimize the time execution of the model. The performance of auto-encoders in terms
of the classification accuracy was comparable but their training time was longer. In future
as more advancements come in the field of deep learning for optimizing the algorithms
execution time, it may outperform other dimensionality reduction methods.
