Data processing method and device, terminal and storage medium

Abstract

The invention discloses a data processing method and device, a terminal and a storage medium, and belongs to the technical field of data processing. The method comprises the steps of obtaining to-be-processed data; calling a low-rank representation and subspace joint model to process the to-be-processed data, wherein the low-rank representation and subspace joint model comprises a low-rank model for denoising and a regularization item for dimensionality reduction; and carrying out joint solution on the low-rank model and the regularization item to obtain de-noised and dimensionally reduced data. The to-be-processed data is processed at the same time by adopting the low-rank representation and subspace joint model, and the low-rank model and the regularization item are jointly solved in theprocessing process. As the low-rank model and the regularization item are respectively used for denoising and dimensionality reduction, the solving process can be optimized on two targets of denoising and dimensionality reduction at the same time, the optimal global optimization effect is realized, and the effects of data denoising and dimensionality reduction are ensured.

Description

technical field [0001] The present invention relates to the field of data processing, in particular to a data processing method, device, terminal and storage medium. Background technique [0002] With the continuous maturity of machine learning technology, data classification or clustering has become one of the important research directions. Taking data classification processing as an example, the data to be classified needs to be preprocessed before data classification. Among them, the preprocessing process usually includes two parts: data denoising and data dimensionality reduction. [0003] Among them, data denoising refers to removing noise in the data to be classified and recovering clean data. Data dimensionality reduction refers to reducing the number of random variables in the data and selecting the main variables for data classification. [0004] In related technologies, data denoising and data dimensionality reduction are usually implemented by using two functio...

AI Technical Summary

Problems solved by technology

When performing data denoising and data dimensionality reduction, the two functions are optimized independently. The data dimensionality reduction process is not considered when the data denoising function is optimized, and the data denoising process is not considered when the data dimensionality reduction function is optimized. A function cannot be optimized collaboratively, and can only achieve a local optimum in the end, but cannot achieve a global optimum result, resulting in an unsatisfactory final data preprocessing effect

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