Sparse Gaussian Process regression model based on ℓ 1/2 regularization

Gaussian Process (GP) model is an elegant tool for the probabilistic prediction. However, the high computational cost of GP prohibits its practical application on large datasets. To address this issue, this paper develops a new sparse GP model, referred to as GPHalf. The key idea is to sparsify the GP model via the newly introduced ? _1/2 regularization method. To achieve this, we represent the GP as a generalized linear regression model, then use the modified ? _1/2 half thresholding algorithm to optimize the corresponding objective function, thus yielding a sparse GP model. We proof that the proposed model converges to a sparse solution. Numerical experiments on both artificial and real-world datasets validate the effectiveness of the proposed model.     

Salient object detection with low-rank approximation and ℓ2,1-norm minimization

Salient object detection is an important issue in computer vision and image procession in that it can facilitate humans to locate conspicuous visual regions in complex scenes rapidly and improve the performance of object detection and video tracking. In recent years, low-rank matrix approximation has been proved to be favorable in image saliency detection and gained a great deal of attention. An underlying assumption of low-rank recovery is that an image is a combination of background regions being low-rank and salient objects being sparse, which corresponds to tough non-smooth optimization problems. In this paper, by incorporating ℓ_2,1-norm minimization, we obtain the corresponding smooth optimization problems and propose two effective algorithms with proved convergence. To guarantee the robustness of the proposed methods, the input image is divided into patches and each patch is approximately represented by its mean value. Besides, multi-scale visual features of each patch of the given image are extracted to capture common low-level features such as color, edge, shape and texture. The salient objects of a given image are indicated with sparse coefficients solved by the low-rank matrix approximation problem. Saliency maps are further produced with integration of the high-level prior knowledge. Finally, extensive experiments in four real-world datasets demonstrate that the proposed methods come with competitive performance over the eight compared state-of-the-arts.     

Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification

Neural Computing and Applications - Cognitive impairment must be diagnosed in Alzheimer’s disease as early as possible. Early diagnosis allows the person to receive effective treatment...     

Reduced-order ℓ2-ℓ∞ filtering for discrete-time T-S fuzzy systems with stochastic perturbation

This paper is concerned with the problem of ?₂-?_∞ filtering for discrete-time Takagi-Sugeno (T-S) fuzzy systems with stochastic perturbation. Firstly, a basis-dependent existence condition of desirable ?₂-?_∞ filters is proposed. Then by means of the convex linearisation technique, the derived condition is transformed into some strict linear matrix inequality (LMI) constraints, by which both full- and reduced-order filters can be designed. Moreover, for the reduced-order ?₂-?_∞ filter design, a novel analysis and design method with the projection lemma is also provided. Finally, a numerical example is provided to illustrate the feasibility and effectiveness of the proposed full- and reduced-order ?₂-?_∞ filter design methods.     

Real-time state-of-health monitoring of lithium-ion battery with anomaly detection,Levenberg–Marquardt algorithm,and multiphase exponential regression model

Neural Computing and Applications - The state of health (SOH) of lithium-ion (Li+) battery prediction plays significant roles in battery management and the determination of the durability of the...