In a recent paper [2] we introduced the greedy Gradient Pursuit framework. This is a family of algorithms designed to find sparse solutions to underdetermined inverse problems. One particularly powerful member of this family is the (approximate) conjugate gradient pursuit algorithm, which was shown to be applicable to very large data sets and which was found to perform nearly as well as the tra...

In this research, we propose to use the discrete cosine transform to approximate the cumulative distributions of data cube cells’ values. The cosine transform is known to have a good energy compaction property and thus can approximate data distribution functions easily with small number of coefficients. The derived estimator is accurate and easy to update. We perform experiments to compare its ...

An algorithm is presented for constructing an approximate numerical solution to a large scale Lyapunov equation in low rank factored form. The algorithm is based upon a synthesis of an approximate power method and an alternating direction implicit (ADI) method. The former is parameter free and tends to be efficient in practice, but there is little theoretical understanding of its convergence pr...

Abstract At present, many cloning selection algorithms have been studied, and improvements made to the cloning, mutation steps. However, there is a lack of research on optimization updating operation The clonal algorithm traditionally updated through random complement antibodies, which blind uncertain process. added antibodies may gather near local optimal solution, resulting in need for more i...

Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). To address the problem of outlier detection in wireless sensor networks, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient approach for detecting outliers in sensed data in a WSN. The outliers in sensed data can be ca...

In varying coefficient models, three types of variable selection problems are of practical interests: separation of varying and constant effects, selection of variables with nonzero varying effects, and selection of variables with nonzero constant effects. Existing variable selection methods in the literature often focus on only one of the three types. In this paper, we develop a unified variab...