Dynamic hypergraph structure learning classification method and system based on tensor representation

Abstract

The invention discloses a dynamic hypergraph structure learning classification method and system based on tensor representation, and the method comprises the steps: 1, extracting a feature vector of sample data in a database, constructing a hypergraph structure according to the feature vector, and representing the connection strength between any point set in the hypergraph structure through the tensor; 2, introducing a potential energy loss function and an experience loss function in a label vector set in the database, a hypergraph structure expressed by tensor and a potential energy of the point set, generating a dynamic hypergraph structure learning model, performing optimization solution on the dynamic hypergraph structure learning model by using an alternating optimization method, andapplying an optimal solution of a label vector set after model solution to data classification. According to the method, the tensor is introduced to serve as the representation form of the dynamic hypergraph structure and the dynamic hypergraph structure learning method, the hypergraph structure and the label vector of the data are alternately optimized, and finally data classification is achievedaccording to the optimal solution of the label vector of the data.

Description

technical field [0001] The present application relates to the technical field of data label processing, in particular, to a tensor representation-based dynamic hypergraph structure learning classification method and a tensor representation-based dynamic hypergraph structure learning classification system. Background technique [0002] In practical applications, usually a small part of the data is labeled and most of the data is unlabeled. In such cases, semi-supervised learning methods can utilize both labeled and unlabeled data, showing excellent performance. [0003] Hypergraph is a semi-supervised classification method, each vertex of which represents a sample data, and hyperedge represents the relationship between sample data. Unlike simple graphs, a hyperedge can connect multiple vertices. Therefore, hypergraph can represent Complex high-order associations of data. [0004] The learning method based on the hypergraph structure can be considered as a process of label p...

AI Technical Summary

Problems solved by technology

However, this adjustment does not fully fix inappropriate or even wrong connections, thus improving the performance of the hypergraph structure only marginally.

Images