Realizing high performance of ordinary robots is one of the core problems in robotic
research. Improving the performance of ordinary robots usually relies on the collaborative …

This paper proposes a novel approach for learning a data-driven quadratic manifold from
high-dimensional data, then employing this quadratic manifold to derive efficient physics …

In this article, we present a general optimization framework that leverages structured sparsity
to achieve superior recovery results. The traditional method for solving the structured sparse …

Hyperspectral anomaly detection (AD) is an important problem in remote sensing field. It can
make full use of the spectral differences to discover certain potential interesting regions …

This paper introduces a graph-based semi-supervised embedding method as well as its
kernelized version for generic classification and recognition tasks. The aim is to combine the …

In this paper, we address the semisupervised distance metric learning problem and its
applications in classification and image retrieval. First, we formulate a semisupervised …

The support vector machine (SVM) method, as a promising classification technique, has
been widely used in various fields due to its high efficiency. However, SVM cannot …

With the progress of financial technology (FinTech), real-time information from FinTech is
huge and complicated. For various fields of research, identifying intrinsic features of complex …

Multi-modal image registration is the primary step in integrating information stored in two or
more images, which are captured using multiple imaging modalities. In addition to intensity …

Identification of intrinsic characteristics and structure of high-dimensional data is an
important task for financial analysis. This paper presents a kernel entropy manifold learning …