no code implementations • 22 Jan 2024 • Hong Wei, James Xiao, Yichao Zhang, Xia Hong
This suggests that, in order to account for spatial correlation between pixels, a feature vector associated with each pixel may be a vectorized tensor representing the multiple bands and a local patch as appropriate.
no code implementations • 2 Sep 2023 • Alejandro Rodriguez Dominguez, Muhammad Shahzad, Xia Hong
A closed-form solution with least-squares is presented, which to the authors knowledge, is the fastest solution in the literature for multiple hypotheses and structured predictions.
no code implementations • 23 Feb 2022 • Jingxin Zhang, Donghua Zhou, Maoyin Chen, Xia Hong
In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring.
no code implementations • 13 Dec 2020 • Jingxin Zhang, Maoyin Chen, Hao Chen, Xia Hong, Donghua Zhou
By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring.
no code implementations • 12 Dec 2020 • Jingxin Zhang, Hao Chen, Songhang Chen, Xia Hong
To realize this purpose, the technique of a mixture of probabilistic principal component analysers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach.
no code implementations • 15 Nov 2019 • Dai Shi, Junbin Gao, Xia Hong, S. T. Boris Choy, Zhiyong Wang
These geometrical features of CMM have paved the way for developing numerical Riemannian optimization algorithms such as Riemannian gradient descent and Riemannian trust-region algorithms, forming a uniform optimization method for all types of OT problems.
no code implementations • 29 Jan 2019 • Di Xu, Manjing Fang, Xia Hong, Junbin Gao
A general framework of least squares support vector machine with low rank kernels, referred to as LR-LSSVM, is introduced in this paper.
no code implementations • 4 Sep 2015 • Xia Hong, Sheng Chen, Yi Guo, Junbin Gao
A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is proposed based on the concept of leaveone- out mean square error (LOOMSE).
no code implementations • 18 Aug 2015 • Yifan Fu, Junbin Gao, Xia Hong, David Tien
In this paper, we present a novel low rank representation (LRR) algorithm for data lying on the manifold of square root densities.
no code implementations • 7 Apr 2015 • Yanfeng Sun, Junbin Gao, Xia Hong, Bamdev Mishra, Bao-Cai Yin
In contrast to existing techniques, we propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model.