Unsupervised Learning: Dimensionality Reduction

Unsupervised Learning: Dimensionality Reduction

The main reason for dimensiionality reduction is to compress the data such that our algorithms can learn properly and their computational cost is minimum.

If we allow ourselves to approximate the original data set by projecting all of my original examples onto this green line over here, then I need only one number.

I need only real number to specify the position of a point on the line, and so what I can do is therefore use just one number to represent the location of each of my training examples after they've been projected onto that green line.

So this is an approximation to the original training self because I have projected all of my training examples onto a line. But now, I need to keep around only one number for each of my examples.

And so this halves the memory requirement, or a space requirement, or what have you, for how to store my data.

Now  let's say we have our 10000 3D data to 100 2D data how we wil do that See the images:


So now we just require Z1 and Z2 to represent our data :


Now these all are about compression of data 
Lets say we have many features so how do we visualize them for that we use data visualization technique 










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