Discovering Multiple Constraints that are Frequently Approximately Satisfied
This work addresses data modeling challenges in high-dimensional settings, but it appears incremental as it builds on existing constraint-based approaches without claiming broad breakthroughs.
The paper tackles the problem of modeling high-dimensional datasets by assuming many different linear constraints that are frequently approximately satisfied, proposing three methods to learn products of constraints using heavy-tailed probability distributions for violations.
Some high-dimensional data.sets can be modelled by assuming that there are many different linear constraints, each of which is Frequently Approximately Satisfied (FAS) by the data. The probability of a data vector under the model is then proportional to the product of the probabilities of its constraint violations. We describe three methods of learning products of constraints using a heavy-tailed probability distribution for the violations.