Semi-supervised multi-mark distance metric learning method fusing local metric

Abstract

The invention discloses a semi-supervised multi-mark distance measurement learning method fusing local measurement, and the method comprises the steps: extracting training data of any multi-mark application scene, and marking a small amount of data; for labeled data, filtering out samples with the labeling occupancy smaller than a set threshold value, removing abnormal points of unlabeled data through clustering and other operations, and improving the sample quality; expressing the distance metric to be learned as a combined distance metric form; constructing an optimization item based on thedistance metric representation; constructing a multi-mark loss item and manifold regularization item joint optimization model, and learning distance measurement; mapping the original data to a distance measurement space, and then performing learning by using an existing semi-supervised multi-mark learning algorithm to obtain a semi-supervised multi-mark classifier fused with local measurement; andinputting a to-be-predicted sample into the classifier to obtain a labeled sample. By adopting the method, the manual labeling cost can be reduced, and the practical application of the multi-label learning framework is promoted.

Description

technical field [0001] The invention is applicable to any multi-label learning scene with a small amount of labeled data and a large amount of unlabeled data, and in particular relates to a semi-supervised multi-label distance metric learning method with fusion of local metrics. Background technique [0002] In recent years, multi-label learning has attracted extensive attention from researchers for its ability to model objects with rich semantic information and a large number of research results have emerged. At present, multi-label learning algorithms mainly consider the correlation between labels from the label space, and do less processing on the feature space. [0003] There are a lot of unlabeled data in practical applications, and obtaining object labels requires a lot of manpower and material resources. Based on this, semi-supervised multi-label learning is proposed. This learning scenario includes a small amount of labeled multi-label data and a large amount of unla...

AI Technical Summary

Problems solved by technology

At present, multi-label learning algorithms mainly consider the correlation between labels from the label space, and do less processing of the feature space.

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