Search

Showing total 2 results
2 results

1. A fast algorithm to solve large-scale matrix games based on dimensionality reduction and its application in multiple unmanned combat air vehicles attack-defense decision-making.

Author

Li, Shouyi, Chen, Mou, Wang, Yuhui, and Wu, Qingxian

Subjects
*ARMORED military vehicles, *LINEAR programming, *NASH equilibrium, *ALGORITHMS, *MATRICES (Mathematics)
Abstract

In the scenario of attack-defense involved with unmanned combat air vehicles (UCAVs), it is often envisioned that a large group of UCAVs is deployed to complete some complex tasks which could be specifically modeled as large-scale matrix games. Solving such matrix games by the traditional linear programming approaches, however, could be quite time-consuming and thus cannot be implemented in real-time which is, in fact, a key requirement for real air combat. On this account, an algorithm, termed as dimensionality reduction based matrix game solving algorithm (DR-MG), is proposed in this paper to solve large-scale matrix games in a timely manner. Our algorithm builds on the technique of dimensionality reduction which inherently finds the convex hull vertices of a vector set. Through establishing the connection between Nash equilibria of the matrix games before and after dimensionality reduction, the proposed algorithm is capable of finding the solutions while only dealing with the matrix game with reduced dimensions. As a consequence, it is expected the time complexity of the proposed algorithm is significantly decreased, and thus the algorithm could be applicable in real air combat. Finally, numerical results are provided to show the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

2. Multi-view [formula omitted]-means clustering algorithm based on redundant and sparse feature learning.

Author

Kong, Guoping, Ma, Yingcang, Xing, Zhiwei, and Xin, Xiaolong

Subjects
*ALGORITHMS, *COSINE function, *SPARSE approximations, *FEATURE selection, *FUZZY algorithms, *MATRICES (Mathematics)
Abstract

In recent years, multi-view clustering has been widely concerned and studied. Being its data generally has high dimension and noise, how to effectively deal with redundant features in data from various views and improve the clustering effect is an important issue in multi-view clustering. In this paper, a robust multi-view clustering algorithm Multi-view K -means clustering algorithm based on redundant and sparse feature learning (RSFMVKM) considering redundancy and sparse feature learning is proposed, which can effectively reduce the dimensions of data from different views. Firstly, the K -means algorithm is extended to multi-view clustering, and the importance of each views is represented by learning the weight of each view. Secondly, the sparse feature representation is obtained by applying the norm constraint to the projection matrix in a single view, and the cosine similarity is used to depict the redundancy matrix, which can remove the redundant information and improve the clustering effect. Moreover, the proposed algorithm can effectively avoid the generation of empty classes, and the clustering index matrix is discrete, so the clustering results can be obtained directly. Finally, the proposed algorithm is compared with six state-of-the-art multi-view clustering algorithms on six public datasets, and the experimental results show that the algorithm is effective. • By feature learning, the dimensionality of data is effectively reduced and redundant features are removed. • Control the importance of each view by learning weights. • Using the coordinate descent method, the clustering results can be obtained directly and no empty class is generated. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF