This paper proposes a symmetric alternating direction method of multipliers with two different relaxation factors for solving nonconvex optimization problems with linear constraints and a ...

Abstract: The identification of separable nonlinear models, prevalent in tasks such as signal analysis, image processing, time series analysis, and machine learning, presents a non-convex optimization ...

Note: Nonnegative Matrix Factorization is an area of active research. New algorithms are proposed every year. Contributions are very welcomed. Most types and functions (except the high-level function ...

The present study combined a supervised machine learning framework with an unsupervised method, finite mixture modeling, to identify prognostically meaningful subgroups of diverse chronic pain ...

Marshall, a Mississippi native, is a dedicated IT and cybersecurity expert with over a decade of experience. Along with Techopedia, his articles can be found… This mapping is done through kernel ...

We discuss structure-preserving model order reduction for port-Hamiltonian systems based on a nonlinear approximation ansatz which is linear with respect to a part of the state variables of the ...

Abstract: Separable nonlinear models (SNLMs) are of great importance in system modeling, signal processing, and machine learning because of their flexible structure and excellent description of ...

We introduce a supervised learning framework for target functions that are well approximated by a sum of (few) separable terms. The framework proposes to approximate each component function by a ...

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