5 citations · 7 across the 4 of their papers we have counts for
7 papers
A Decentralized Framework for Kernel PCA with Projection Consensus Constraints
Fan He, Ruikai Yang, Lei Shi +1
This paper studies kernel PCA in a decentralized setting, where data are distributively observed with full features in local nodes and a fusion center is prohibited. Compared with…
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation
Mingzhen He, Fan He, Lei Shi +2
Asymmetric kernels naturally exist in real life, e.g., for conditional probability and directed graphs. However, most of the existing kernel-based learning methods require kernels…
Fast algorithms for robust principal component analysis with an upper bound on the rank
Ningyu Sha, Lei Shi, Ming Yan
The robust principal component analysis (RPCA) decomposes a data matrix into a low-rank part and a sparse part. There are mainly two types of algorithms for RPCA. The first type of…
Analysis of Regularized Least Squares in Reproducing Kernel Krein Spaces
Fanghui Liu, Lei Shi, Xiaolin Huang +2
In this paper, we study the asymptotic properties of regularized least squares with indefinite kernels in reproducing kernel Krein spaces (RKKS). By introducing a bounded hyper-sph…
Sub/super-stochastic matrix with applications to bipartite tracking control over signed networks
Lei Shi, Wei Xing Zheng, Jinliang Shao +1
In this contribution, the properties of sub-stochastic matrix and super-stochastic matrix are applied to analyze the bipartite tracking issues of multi-agent systems (MASs) over si…
Convergence of Unregularized Online Learning Algorithms
Yunwen Lei, Lei Shi, Zheng-Chu Guo
In this paper we study the convergence of online gradient descent algorithms in reproducing kernel Hilbert spaces (RKHSs) without regularization. We establish a sufficient conditio…