A Convex Formulation for Spectral Shrunk Clustering

Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance.Comment: AAAI201

Evolutionary history and stress regulation of the lectin superfamily in higher plants

10.1186/1471-2148-10-79BMC Evolutionary Biology101

Multi-objective integrated robust H∞ control for attitude tracking of a flexible spacecraft

This paper investigates the multi-objective attitude tracking problem of a flexible spacecraft in the presence of disturbances, parameter uncertainties and imprecise collocation of sensors and actuators. An integrated robust H∞ controller, including an output feedback component and a feedforward component, is proposed, and its gains are calculated by solving Linear Matrix Inequalities. The output feedback component stabilizes the integrated control system while the feedforward component can drive the attitude motion to track the desired angles. The system robustness against disturbances, parameter uncertainties and imprecise collocation is addressed by the H∞ approach and convex optimization. Numerical simulations are finally provided to assess the performance of the proposed controller