DRASO: Declaratively Regularized Alternating Structural Optimization
نویسندگان
چکیده
Recent work has shown that Alternating Structural Optimization (ASO) can improve supervised learners by learning feature representations from unlabeled data. However, there is no natural way to include prior knowledge about features into this framework. In this paper, we present Declaratively Regularized Alternating Structural Optimization (DRASO), a principled way for injecting prior knowledge into the ASO framework. We also provide some analysis of the representations learned by our method.
منابع مشابه
Linearized Alternating Direction Method of Multipliers for Constrained Nonconvex Regularized Optimization
In this paper, we consider a wide class of constrained nonconvex regularized minimization problems, where the constraints are linearly constraints. It was reported in the literature that nonconvex regularization usually yields a solution with more desirable sparse structural properties beyond convex ones. However, it is not easy to obtain the proximal mapping associated with nonconvex regulariz...
متن کاملSome Convergence Results on the Regularized Alternating Least-Squares Method for Tensor Decomposition
We study the convergence of the Regularized Alternating Least-Squares algorithm for tensor decompositions. As a main result, we have shown that given the existence of critical points of the Alternating Least-Squares method, the limit points of the converging subsequences of the RALS are the critical points of the least squares cost functional. Some numerical examples indicate a faster convergen...
متن کاملAn Augmented Lagrangian Method for ℓ1-Regularized Optimization Problems with Orthogonality Constraints
A class of `1-regularized optimization problems with orthogonality constraints has been used to model various applications arising from physics and information sciences, e.g., compressed modes for variational problems. Such optimization problems are difficult to solve due to the non-smooth objective function and nonconvex constraints. Existing methods either are not applicable to such problems,...
متن کاملAn ADMM Algorithm for Solving l_1 Regularized MPC
We present an Alternating Direction Method of Multipliers (ADMM) algorithm for solving optimization problems with an `1 regularized least-squares cost function subject to recursive equality constraints. The considered optimization problem has applications in control, for example in `1 regularized MPC. The ADMM algorithm is easy to implement, converges fast to a solution of moderate accuracy, an...
متن کاملOptimization of Solution Regularized Long-wave Equation by Using Modified Variational Iteration Method
In this paper, a regularized long-wave equation (RLWE) is solved by using the Adomian's decomposition method (ADM) , modified Adomian's decomposition method (MADM), variational iteration method (VIM), modified variational iteration method (MVIM) and homotopy analysis method (HAM). The approximate solution of this equation is calculated in the form of series which its components are computed by ...
متن کامل