"A common example that I use with people to explain complications in dimensionality is dropping a pin on a 10-foot straight line. This would be relatively simple to find. Next, if you dropped the pin in a 10 foot by 10-foot square, the task of finding the pin becomes more difficult. Adding a third dimension to make a 10 foot cubed area makes it more difficult to find the pin if placed within it. In bringing this back to machine learning, my job is to somehow make the three-dimensional field that the machine will pull from easier to pull from. Last year, I was part of a team that developed a system for pulling public health data. We set many variance thresholds that removed values that didn't change much from observation to observation. After careful testing, the system was able to pull information quickly and accurately based on these thresholds."
