Reweighted multi-view clustering with tissue-like P system

Abstract

Multi-view clustering has received substantial research because of its ability to discover heterogeneous information in the data. The weight distribution of each view of data has always been difficult problem in multi-view clustering. In order to solve this problem and improve computational efficiency at the same time, in this paper, Reweighted multi-view clustering with tissue-like P system (RMVCP) algorithm is proposed. RMVCP performs a two-step operation on data. Firstly, each similarity matrix is constructed by self-representation method, and each view is fused to obtain a unified similarity matrix and the updated similarity matrix of each view. Subsequently, the updated similarity matrix of each view obtained in the first step is taken as the input, and then the view fusion operation is carried out to obtain the final similarity matrix. At the same time, Constrained Laplacian Rank (CLR) is applied to the final matrix, so that the clustering result is directly obtained without additional clustering steps. In addition, in order to improve the computational efficiency of the RMVCP algorithm, the algorithm is embedded in the framework of the tissue-like P system, and the computational efficiency can be improved through the computational parallelism of the tissue-like P system. Finally, experiments verify that the effectiveness of the RMVCP algorithm is better than existing state-of-the-art algorithms.

Hash3D: Training-free Acceleration for 3D Generation

“…To mitigate this bottleneck, current efforts concentrate on three strategies. The first strategy trains an inference-only models [7,11,18,24,56] to bypass the lengthy optimization process. While effective, this method requires extensive training time and substantial computational resources.…”

An Extended Membrane System with Monodirectional Tissue-like P Systems and Enhanced Particle Swarm Optimization for Data Clustering

“…MAs based on TPs have been merged with various heuristic algorithms, such as genetic algorithm (GA) [21], differential evolution (DE) [22] and its variations [23], particle swarm optimization (PSO) [24] and its variations [25], ant colony optimization (ACO) [26], and artificial bee colony algorithm (ABC) [27], to comprehensively utilize the strengths of heuristic algorithms with stronger practicality and high robustness, as well as the low complexity and effectiveness of TPs. MAs based on TPs have been successfully applied to resolve various advanced problems in the world [28][29][30][31].…”