Supervised linear dimensionality reduction method with separation probability of minimax pobability machine

Abstract

The invention provides a supervised linear dimensionality reduction method with a separation probability of a minimax probability machine and belongs to the technical field of computer machine learning and statistical learning. The method comprises the steps that: a supervised linear dimensionality reduction model with the separation probability of the minimax probability machine is established, an input of the model is a sample set with multiple dimensions and categories, and an output is a projection matrix, when the dimensions are reduced to 1 dimension, an object belongs to a single projection vector object, when the dimensions are reduced to multiple dimensions, objects belong to multiple projection vector objects. According to the method, a separation probability between samples is used as a distance measurement between the categories, a conjugate gradient method is used for optimization, and finally, each category pair has a projection matrix of a maximum separation probabilityas far as possible. According to the method, the distinguishability of the data and the accuracy and efficiency of the subsequent classification can be improved, and a good application effect can be achieved in the problems of multiple types of dimension reduction.

Description

technical field [0001] The invention belongs to the technical field of computer machine learning and statistical learning, and in particular relates to a supervised linear dimensionality reduction method using the separation probability of a minimum maximum probability machine. Background technique [0002] In the field of machine learning and metric learning, the role of dimensionality reduction methods is very important. Dimensionality reduction methods can map high-dimensional data into a low-dimensional subspace, while preserving the separation information between samples (unsupervised learning) or categories (supervised learning) as much as possible. It is often used as data preprocessing to improve subsequent data analysis, such as classifiers, data visualization, and regression. [0003] Linear Discriminant Analysis (LDA) is a classic feature extraction and dimensionality reduction method based on supervised distance measures. LDA was originally proposed by Fisher e...

AI Technical Summary

Problems solved by technology

For example, although LDA can be used in the dimensionality reduction scenario of multi-class problems, it has an inherent shortcoming: it depends on the total intra-class dispersion and the total inter-class dispersion to obtain the projection matrix, and its distance measurement form, We call this the "sum of squares" form

However, although these methods overcome the shortcomings of LDA in multi-class scenarios to a certain extent, they are usually heuristic, and their objective functions lack an accurate practical meaning.

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