Performance Analysis of Dimensionality Reduction Techniques for Datasets using Deep Learning

Dimensionality reduction techniques play a crucial role in handling high-dimensional datasets by transforming data into a lower-dimensional space while preserving essential information. Deep learning, a powerful subset of machine learning, has shown remarkable success in various domains. Dimensionality reduction strategies can be used to lessen the complexity between various qualities. With these techniques, a lower-dimensional dataset is created from the dimensions of the initial input dataset. Feature extraction and feature selection are the two primary categories of dimensionality reduction techniques. In contrast to the feature selection method, which selects a subset of features from the original set to create a smaller subset that can be used to build the model, the feature extraction method reduces the high-dimensional dataset to a lower dimension space, that is, a space with fewer characteristics with different values from the original dataset. We've examined a variety of dimensionality reduction techniques, including traditional methods like PCA, LDA and MDS, throughout data processing, to minimize the dimensionality of a sizable and complicated dataset. Additionally, we talked about numerous methods for reducing the dimensionality of data and contrasted them according to different factors. In conclusion, this research provides valuable insights into the effectiveness of dimensionality reduction techniques for high-dimensional datasets. Additionally, we identify potential areas for future research and improvements in the realm of dimensionality reduction.