# Variance or Standard Deviation

I just found out that I made a mistake in my previous post (it is fixed now). There, I argued that in order to calculate the distance between a point and a set (which has unequal variances along different dimensions), we can use the following formula:

# The Mighty Distance Function – Part 3

So here we are again, with some new proposals for the distance function between a point and a set. In the previous post, we became familiar with the variance measure and two methods to map points into a new space so that the distance function in the new space behaves the same way as what intuitively seems right! Continue reading

# The Mighty Distace Function – Part 2

So in our previous step, we went over some possible approaches to define a distance function between a point and a set. At the end we saw that calculating distance between the point and the average (mean) point of the set seems to be the best possible solution. I am afraid that it is not always that easy. Continue reading

# The Mighty Distance Function – Part 1

I’ll start my posts with the distance function, as I believe a complete understanding of it, provides the basic idea behind most of the ML algorithms. Some of the approaches which come next are closely related to some known algorithms in ML, but I am not going to name any of them, as I believe that a complete understanding of the intuition behind any of them is more important than their names. Continue reading