Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statistics
We propose an extended generalization of the pseudo Huber loss formulation. We show that using the log-exp transform together with the logistic function, we can create a loss which combines the desirable properties of the strictly convex losses with robust loss functions. With this formulation, we show that a linear convergence algorithm can be utilized to find a minimizer. We further discuss the creation of a quasi-convex composite loss and provide a derivative-free exponential convergence rate algorithm.
Feb-22-2022
- Country:
- North America > United States
- New York > New York County > New York City (0.04)
- Europe > United Kingdom
- England > Cambridgeshire > Cambridge (0.04)
- North America > United States
- Genre:
- Research Report (0.40)
- Technology: