Support vector machine sorting method based on simultaneously blending multi-view features and multi-label information

Abstract

The invention discloses a support vector machine sorting method based on simultaneously blending multi-view features and multi-label information. The support vector machine sorting method based on simultaneously blending the multi-view features and the multi-label information comprises the following steps, inputting multi-view feature training data and the multi-label information corresponding to each data, establishing a mathematical model which simultaneously blends the multi-view features and the multi-label information and supports a vector machine classifier, and setting value of a corresponding weight factor of each item. Training and learning each parameter of a classifier, using loop iteration interactive algorithm to update all parameter variables of target optimization formula until absolute value of the difference of whole objective function values of two iterative is less than preset threshold valve, stopping. Meanwhile, when a parameter is adopted, updated and calculated, strategy fixing other parameter values is adopted. The classifier which is obtained by training conducts multi-label classification or precasting on actual data. When technology supports classification of a vector machine, a unified data expression form in a novel data space is learned, and accuracy rate of the classifier is improved.

Description

technical field [0001] The invention belongs to the technical field of labels, and in particular relates to a support vector machine classification method based on simultaneous fusion of multi-view features and multi-label information. Background technique [0002] With the advent of the information age, all kinds of data explode and grow exponentially. Especially in the Internet field, massive cross-media data has become a focus of attention and research. For example, content understanding and extraction of image data combined with text label data has played a vital role in Internet information search and data mining applications. And one of the keys is how to accurately classify the content of images with multiple characteristics. These features can be obtained through various feature extraction techniques, such as image sift feature extraction technology, HUE image color feature extraction technology, and Gabor image texture extraction technology. In text analysis and ...

AI Technical Summary

Problems solved by technology

Moreover, the existing multi-view information feature classifier directly uses the data features obtained in various ways as the input of the classifier, which brings in a lot of noise interference and redundant information, which greatly affects the classification results.

Images