1- Implementation of PCA from scratch.
a) implementation of covariance matrix.
b) implementation of eigenvalues and eigenvectors using power iteration method.
2- Preprocessing the mnist dataset making it binary image (only zeros and ones) to apply the hamming network later on it.
3- Tring different number of components in PCA till gets best result.
4- Cluster data using k-means (you can replace it with any clustering technique).
5- Apply Hamming on unseen data point with PCA and without PCA.
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Hamming Network implementation using PCA implementation from scratch
Topics
pca
insertion-sort
mnist-dataset
eigenvectors
unsupervised-learning
kmeans-clustering
eigenvalues
k-means-implementation-in-python
k-means-clustering
kmeans-clustering-algorithm
variance-analysis
hamming-network
pca-implementation
power-iteration
covariance-matrix-implementation
eigenvalues-implementation
eigenvectors-implementation
mnist-preprocessing
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