Signal Decomposition Using Masked Proximal Operators

TL;DR

This paper introduces a flexible framework for decomposing vector time series into various components using masked proximal operators, providing distributed algorithms for convex and non-convex loss functions.

Contribution

It presents a general, unified approach for signal decomposition via loss functions and develops distributed optimization methods utilizing masked proximal operators.

Findings

Methods find optimal decomposition for convex loss functions.

Heuristic methods perform well for non-convex loss functions.

New tractable techniques for masked proximal operators of specific loss functions.

Abstract

We consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We describe a simple and general framework in which the components are defined by loss functions (which include constraints), and the signal decomposition is carried out by minimizing the sum of losses of the components (subject to the constraints). When each loss function is the negative log-likelihood of a density for the signal component, this framework coincides with maximum a posteriori probability (MAP) estimation; but it also includes many other interesting cases. Summarizing and clarifying prior results, we give two distributed optimization methods for computing the decomposition, which find the optimal decomposition when the component class loss functions are convex, and are good heuristics when they are not. Both methods require only the masked proximal operator of each of the component loss functions, a generalization of the well-known proximal operator that handles missing entries in its argument. Both methods are distributed, i.e., handle each component separately. We derive tractable methods for evaluating the masked proximal operators of some loss functions that, to our knowledge, have not appeared in the literature.