Inductive non-negative projection semi-supervised data classification method and system

Abstract

The method of the invention definitely combines semi-supervised data representation and classification errors into a conventional non-negative matrix factorization framework for joint minimization learning, thereby applying weight coefficient construction and a label propagation process to the projection non-negative matrix factorization, avoiding the negative effects of noise, damage, or aliens included in original data on similarity measures and label prediction results. In addition, the joint minimization process can maintain the neighborhood information and the spatial structure in the projection non-negative matrix factorization process, and obtain an accurate data representation result. In addition, the weight construction and the inductive learning are integrated into a unified model so as to obtain an adaptive weight coefficient matrix, thereby avoiding the difficulty in selecting the optimal neighbor in a traditional algorithm. The method of the invention is an inductive model, can complete the induction and prediction of the data outside a sample, prevents introduction of additional reconstruction process, and has good scalability performance.

Description

technical field [0001] The invention relates to an inductive non-negative projection semi-supervised data classification method and system, belonging to the technical fields of pattern recognition and data mining. Background technique [0002] Graph-based semi-supervised learning has been an important topic in the fields of data mining and pattern recognition. Since the model can learn from a small amount of labeled data and a large amount of unlabeled data, it is very suitable for the characteristics of practical application data. Existing models can be broadly categorized into transductive learning and inductive learning based on whether they can be efficiently extended to out-of-sample new data. [0003] Label propagation, as a typical classification model, has attracted considerable attention and interest in academia in recent years. Existing typical transductive label propagation algorithms include Gaussian field and harmonic functions, local and global consensus lear...

AI Technical Summary

Problems solved by technology

Although the existing inductive label propagation methods effectively solve the out-of-sample problem through embedding, there are still obvious shortcomings. Most of the data in practical applications usually contain redundancy or various noises, which have a great impact on classification performance.

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