In the morning, there was a talk given by Subhadeep Mukhopadhyay (Deep) about the “LP Comoment : Concepts and Applications—Finding Patterns in Large Data Sets”. It’s a pretty interesting talk. Two things I want to share here:
One is: “Noise has no pattern, whatever the noise is.” Here since we are looking for patterns from the data. If there is a mechanism which can identify the pattern correctly whatever the noise is, then it is absolutely a good mechanism. From the talk, at least the speaker claimed, the method they proposed can make this happen, which is pretty cool.
The other is about the two cultures Breiman (2001) reminded statisticians awareness of:
1. Parametric modeling culture, pioneered by R.A.Fisher and Jerzy Neyman;
2. Algorithmic predictive culture, pioneered by machine learning research.
Now the speaker claimed that their method is the third one: Nonparametric , Quantile based, Information Theoretic Modeling.
Thus based on the above two things, I am really interested in their method. The following is what I want to study:
Emanuel Parzen wrote lots of papers about this, which is related with the quantize theory.
【update】There is a paper about this topic written by the speaker.