28 citations · 44 across the 13 of their papers we have counts for
13 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…
Coefficient-based Regularized Distribution Regression
Yuan Mao, Lei Shi, Zheng-Chu Guo
In this paper, we consider the coefficient-based regularized distribution regression which aims to regress from probability measures to real-valued responses over a reproducing ker…
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…
Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks
Zhuoqing Song, Lei Shi, Shi Pu +1
In this paper, we propose two communication efficient decentralized optimization algorithms over a general directed multi-agent network. The first algorithm, termed Compressed Push…
Inferring Network Structures via Signal Lasso
Lei Shi, Chen Shen, Libin Jin +4
Inferring the connectivity structure of networked systems from data is an extremely important task in many areas of science. Most of real-world networks exhibit sparsely connected…
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…
Generalization Properties of hyper-RKHS and its Applications
Fanghui Liu, Lei Shi, Xiaolin Huang +2
This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample exten…
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…
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Zhengchu Guo, Lei Shi, Qiang Wu
Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression…
Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction
Xiaolin Huang, Yan Xia, Lei Shi +4
When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in classical reconstruction methods. For example, in C-arm angiograph…
Pinball Loss Minimization for One-bit Compressive Sensing: Convex Models and Algorithms
Xiaolin Huang, Lei Shi, Ming Yan +1
The one-bit quantization is implemented by one single comparator that operates at low power and a high rate. Hence one-bit compressive sensing (1bit-CS) becomes attractive in signa…