Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{156568,
        author = {Dr Basheer Mohamed },
        title = {A Study on Dimensionality Reduction Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {4},
        pages = {173-180},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156568},
        abstract = {In machine learning classification problems, there are often too many factors on the basis of which the final classification is done. These factors are basically variables called features. The higher the number of features, the harder it gets to visualize the training set and then work on it. Sometimes, most of these features are correlated, and hence redundant. This is where dimensionality reduction algorithms come into play. Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.},
        keywords = {},
        month = {},
        }