Fast spectral clustering learning with hierarchical bipartite graph for large-scale data

Yang, Xuemin; Yu, Weizhong; Wang, Rong; Zhang, Guohao; Nie, Feiping

doi:10.1016/j.patrec.2018.06.024

Cited by 49 publications

(19 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the distributed cooperative control of multiagent systems has aroused extensive attentions in multiple fields, including wireless communication, robotics, and distributed computation as shown in References . As an important research topic on the distributed control, formation control refers to design a control strategy with the neighboring information such that a group of autonomous agents reach an expected geometrical shape.…”

Section: Introductionmentioning

confidence: 99%

Robust time‐varying formation design for multiagent systems with disturbances: Extended‐state‐observer method

Wang

et al. 2020

Intl J Robust & Nonlinear

103

View full text Add to dashboard Cite

Robust time-varying formation design problems for second-order multiagent systems subjected to external disturbances are investigated. First, by constructing an extended state observer, the disturbance compensation is estimated, which is a critical term in the proposed robust time-varying formation control protocol. Then, an explicit expression of the formation center function is determined and impacts of disturbance compensations on the formation center function are presented. With the formation feasibility conditions, robust time-varying formation design criteria are derived to determine the gain matrix of the formation control protocol by utilizing the algebraic Riccati equation technique. Furthermore, the tracking performance and the robustness property of multiagent systems are analyzed. Finally, the numerical simulation is provided to illustrate the effectiveness of theoretical results. K E Y W O R D S algebraic Riccati equation, extended state observer, external disturbance, multiagent system, robust time-varying formation 2796

show abstract

Section: Introductionmentioning

confidence: 99%

Robust time‐varying formation design for multiagent systems with disturbances: Extended‐state‐observer method

Wang

et al. 2020

Intl J Robust & Nonlinear

103

View full text Add to dashboard Cite

show abstract

“…In the past decade, extensive concerns of the distributed cooperative control have been aroused by scholars and numerous significant research results have been obtained (e.g. [1–5]) due to its wide applications in different fields, such as formation control of multiple quadrotors and robots [6, 7], coordination for ad hoc networks [8] and clustering learning for large‐scale data [9] etc. As one of the significant research topics on the distributed control, consensus control means to construct consensus control protocols such that a flock of agents can agree on some states of interest.…”

Section: Introductionmentioning

confidence: 99%

Limited‐budget finite‐time average consensus design for multi‐agent systems

Wang

et al. 2020

IET Control Theory & Appl

View full text Add to dashboard Cite

“…Over the past decades, a number of clustering techniques have been developed [6,7,8,9]. One main class of clustering methods is K-means and its various extensions.…”

Section: Introductionmentioning

confidence: 99%

Clustering with similarity preserving

Kang

Wang

et al. 2019

Neurocomputing

View full text Add to dashboard Cite

Graph-based clustering has shown promising performance in many tasks.A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel space can enhance clustering accuracy due to the incorporation of nonlinearity. However, most existing kernel-based graph learning mechanisms is not similarity-preserving, hence leads to sub-optimal performance. To overcome this drawback, we propose a more discriminative graph learning method which can preserve the pairwise similarities between samples in an adaptive manner for the first time. Specifically, we require the learned graph be close to a kernel matrix, which serves as a measure of similarity in raw data. Moreover, the structure is adaptively tuned so that the number of connected components of the graph is exactly equal to the number of clusters. Finally, our method unifies clustering and graph learning which can directly obtain cluster indicators from the graph itself without performing further clustering step. The effectiveness of this approach is examined on both single and multiple kernel learning scenarios in several

show abstract

Fast spectral clustering learning with hierarchical bipartite graph for large-scale data

Cited by 49 publications

References 13 publications

Robust time‐varying formation design for multiagent systems with disturbances: Extended‐state‐observer method

Robust time‐varying formation design for multiagent systems with disturbances: Extended‐state‐observer method

Limited‐budget finite‐time average consensus design for multi‐agent systems

Clustering with similarity preserving

Contact Info

Product

Resources

About