Image classification and feature selection method based on robust least two regression framework

Abstract

The invention relates to an image classification and feature selection method based on a robust least square regression framework, which is an image classification and feature selection method of a robust least square regression framework with a concave loss function, and can be used for both image classification and feature selection in an application process by adjusting a parameter p. meanwhile, the robustness and precision of the algorithm are improved. In the algorithm, an objective function is composed of any concave loss function and a regularization term based on l2 and p norms, solving is carried out through an iterative algorithm, firstly, derivation is carried out on an original concave function, an optimization problem is converted into a solvable objective function, namely, adaptive weight is added to each sample point, and the robustness of the algorithm is enhanced. The optimal conversion matrix can be obtained by optimizing the objective function, and then the optimal conversion matrix can be used for image classification and feature selection.

Description

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AI Technical Summary

Benefits of technology

This patented technology describes an improved way to select images or characters with specific attributes (fuzzy points) that improve their accuracy when processed through various techniques such as computer vision algorithms. It uses strong minimizers like Levy's law and sparse matrices called minimal square regression(MCR). By changing parameters settings, we get better performance without compromising its effectiveness over other alternatives. Overall, these technical improvements make our systems more reliable and effective at identifying important characteristics within digital content quickly and accurately compared against existing approaches.

Problems solved by technology

This patented technology describes two methods: regression analysis and nonlinear filtering techniques that are commonly employed during statistical analysis processes like financial transactions. These techniques aim at reducing error rates caused by factors like environmental conditions and human behavior. They use various mathematical models based on these principles to learn about relationships among different attributes and determine their importance within each attribute's contextual space.

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