- K means algorithm is performed with different initial centroids in
order to get the best clustering.
- The total cost is calculated by summing the distance of each point to
its cluster centre and then summing over all the clusters.
- Based on the minimum overall cost achieved during each iteration of
'iterKMeans' the pixel assignment to their respective clusters are made
and final compressed image is obtained.
- This algorithm will run slower as the number of clusters , size of
the image and number of iterations increase.
Use features of house to predict housing prices
This program uses Logistic regression to classify handwritten digits.
The data is comprised of a part of the MNIST dataset. 5000 examples are
used.
- This program uses Principal Component Analysis to reduce the number
of features used in face recognition.
- This program allows you to set K if you know the number of Principal
components needed or calculates K based on how much variance you would
like to preserve in the images.
- The images consisting of reduced features can be used for training a
neural network or logistic regression model.
- It decreases computation time of the program.
- Original & recovered images displayed