Cross-data-field shift learning sorting method based on differential manifolds

Abstract

The invention discloses a cross-data-field shift learning sorting method based on differential manifolds. The method comprises the steps of inputting data of each data filed and label data used for training and establishing an adjacent map used for spectrogram geometry regulation for the data; establishing an unified mathematical model by combining the input data, label information and the established adjacent map and an optimal objective; deriving an update formula of variables according to the established mathematical model and updating hidden factors of each dimensionality of each data field, inter-domain sharing relational structures and regression coefficients in a mode of alternant iteration until convergence; and carrying out generic label forecasting on data of a target field and obtaining a forecasting generic label of the data of the target field. The cross-data-field shift learning sorting method based on the differential manifolds is used for obtaining a differential data manifold space, a novel expression factor is of a highly- differential structure which is beneficial for sorting, and an original cluster manifold structure of the data is kept.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a cross-data-domain transfer learning classification method based on discriminant manifolds. Background technique [0002] In the information age represented by massive big data, all kinds of data explode and grow exponentially, and the mining of potential value of data has become a hot spot of people's attention and research. Whether it is the Internet, mobile communications, or financial fields, daily life constantly generates a large amount of data, among which classification technology is a very effective way to mine potentially useful knowledge from data. For example, Internet users need to send and receive a large number of emails every day. How to help users sort emails into categories and automatically identify spam requires accurate and effective classification technology to intelligently help users. Another example is how to effectively classify and...

AI Technical Summary

Problems solved by technology

Especially when the classification decision functions of the target data domain and the auxiliary data domain are the same, although the data in the auxiliary domain can be accurately classified, due to the inter-domain offset of the data distribution, the classifier still cannot achieve Ideal classification effect

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