https://github.com/somjit101/dimensionality-reduction-pca-t-sne

Implementation of Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbour Embedding (t-SNE) as Dimensionality Reduction techniques using MNIST Dataset.
https://github.com/somjit101/dimensionality-reduction-pca-t-sne

dimensionality-reduction mnist-dataset mnist-image-dataset pca principal-component-analysis t-sne

Last synced: about 2 months ago
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Implementation of Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbour Embedding (t-SNE) as Dimensionality Reduction techniques using MNIST Dataset.

• Host: GitHub
• URL: https://github.com/somjit101/dimensionality-reduction-pca-t-sne
• Owner: somjit101
• Created: 2021-06-14T19:05:20.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-06-14T19:26:41.000Z (almost 4 years ago)
• Last Synced: 2025-01-16T18:26:39.765Z (3 months ago)
• Topics: dimensionality-reduction, mnist-dataset, mnist-image-dataset, pca, principal-component-analysis, t-sne
• Language: Jupyter Notebook
• Homepage:
• Size: 9.05 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Dimensionality-Reduction-PCA-t-SNE

Implementation of Principal Component Analysis (**PCA**) and t-Distributed Stochastic Neighbour Embedding (**t-SNE**) as Dimensionality Reduction techniques using the [**MNIST**](http://yann.lecun.com/exdb/mnist/) Handwritten Digits Dataset.

## Implementations

* PCA - Implementation from scratch and using [Scikit-Learn PCA object](https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca). 

* t-SNE - Implementation using [Scikit-Learn t-SNE Object](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) for visualization of high-dimensional data on 2D.