On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach
This addresses network inference problems in fields like systems biology, but it is incremental as it builds on existing Sparse Bayesian Learning approaches.
The paper tackles the identification of sparse multivariable ARX models for linear time-invariant networks, proposing a method that infers both Boolean structure and internal dynamics directly from data without prior knowledge, resulting in an algorithm similar to Sparse Group Lasso with efficient solutions.
This paper begins with considering the identification of sparse linear time-invariant networks described by multivariable ARX models. Such models possess relatively simple structure thus used as a benchmark to promote further research. With identifiability of the network guaranteed, this paper presents an identification method that infers both the Boolean structure of the network and the internal dynamics between nodes. Identification is performed directly from data without any prior knowledge of the system, including its order. The proposed method solves the identification problem using Maximum a posteriori estimation (MAP) but with inseparable penalties for complexity, both in terms of element (order of nonzero connections) and group sparsity (network topology). Such an approach is widely applied in Compressive Sensing (CS) and known as Sparse Bayesian Learning (SBL). We then propose a novel scheme that combines sparse Bayesian and group sparse Bayesian to efficiently solve the problem. The resulted algorithm has a similar form of the standard Sparse Group Lasso (SGL) while with known noise variance, it simplifies to exact re-weighted SGL. The method and the developed toolbox can be applied to infer networks from a wide range of fields, including systems biology applications such as signaling and genetic regulatory networks.