A manifold learning system integrating a classic model and used for sample dimension reduction

Abstract

The invention provides a manifold learning system integrating a classic model and used for sample dimension reduction. Firstly, different models developed based on the system are used to reduce the dimension of samples; secondly, the samples which have been reduced in dimension through each method are substituted into a subsequent unified classifier to undergo classification; and then according tothe classification effect, the system selects the model with the best dimension reduction effect. In the test step, the selected model first dimensionally reduces the test samples; and then the processed model is substituted into the subsequent classifier for identification. According to the invention, compared with traditional classification technology, many existing representative manifold learning models are unified through the design of the integral system; selectable models are generated through the system to carry out training of the samples so as to precisely find the model suitable for the present problem; the debugging time is substantially reduced through calculation steps similar in form for integrating different models; and the dimension reduction effect is raised through adoption of an optional measurement mode to generate an incidence matrix.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a manifold learning system used for feature extraction of original samples in the preprocessing process. Background technique [0002] Pattern recognition is the study of using computers to imitate or realize the recognition ability of humans or other animals in order to complete the task of automatic recognition of research objects. In recent years, pattern recognition technology has been widely used in many important fields such as artificial intelligence, machine learning, computer engineering, robotics, neurobiology, medicine, detective science, archaeology, geological exploration, aerospace science and weapon technology. However, with the expansion of the application field, the traditional pattern recognition technology faces new challenges. One of the outstanding challenges comes from the data preprocessing stage. In this stage, the original data processes its ...

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Problems solved by technology

[0005] Aiming at the obvious defects of the existing dimensionality reduction methods, such as poor generalization, narrow application range, and inability to automatically adjust the selection strategy of parameters or criteria, the present invention provides a system capable of integrating three classic and popular learning dimensionality reduction methods. The system first integrates different It is known that popular learning methods are integrated in a framework, and then the selected training samples are subjected to dimensionality reduction processing, and then the most suitable model in the system framework is selected as the formal learning process according to the performance of the processed samples in the subsequent classifier. The preprocessed model in

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