Machine Learning: Apply dimension reduction techniques PCA, t-SNE and UMAP to digit recognizer data set, require pdf and rmd files, do not copy or interpret same to same code from github

Writing

California University of Management and Sciences

Question Description

Context/Background: The central idea is to use ML algorithms to recognize images, both static and dynamic, without specifically providing any guidance to the computer. This task is achieved by training algorithms to recognize various images while also mapping the feature space of those images to a categorical label or name. This task was first successfully achieved in the 1990s when computers were trained to recognize handwritten digits. The trained algorithm was used by postal services to sort mail and efficiently distribute it to the right destination. We will use the same dataset and problem for this case.

The dataset is called MNIST (Modified National Institute of Standards and Technology). More information on MNIST can be found here: Use the only train.csv

https://www.kaggle.com/c/digit-recognizer

To do: The main objective is to write a fully executed R-Markdown program performing dimension reduction on high dimensional image data using MNIST (digits) images that are 28 x 28 pixels resolution. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. Need rmd and pdf for this request. Apply dimension reduction techniques PCA, t-SNE, and UMAP.

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