Method for finishing feature extraction task by utilizing image regularization and data reconstruction

Abstract

The invention discloses a method for finishing a feature extraction task by utilizing image regularization and data reconstruction. The method mainly comprises the following steps of 1) for a group ofdata points, constructing a weight matrix and a corresponding Laplacian matrix; and 2) randomly initializing a feature extraction matrix and a reconstruction coefficient matrix, and iteratively updating the feature extraction matrix and the reconstruction coefficient matrix to obtain a finally converged feature extraction matrix as a feature extraction basis. Compared with a general project recommendation solution, the image regularization is combined with the data reconstruction, so that more effective data features can be extracted. The effect achieved in the data feature extraction problemis better than that in a conventional method.

Description

technical field [0001] The invention relates to community question answering tasks, in particular to a method for solving feature extraction tasks by using image regularization and data reconstruction. Background technique [0002] In tasks such as data mining and information retrieval, the reduction of data dimensionality is a very important task. Reducing the dimensionality of data is of great significance for reducing the time and space consumption of processing data. overfitting phenomenon. The reduction of data dimension usually involves the problem of data feature extraction. For feature extraction, there are currently two methods: supervised feature extraction and unsupervised feature extraction. The supervised feature extraction method requires data to have label information, but currently The amount of data with label information is very small, so the present invention will adopt a non-supervised feature extraction method. [0003] The present invention will use t...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problems in the prior art, in order to overcome the problem that supervised feature extraction in the prior art requires a large amount of labeled data, and the existing unsupervised feature extraction only pays attention to the preservation of data characteristic information, but Without paying attention to the problem of preserving data structure information, the present invention provides a method for solving feature extraction tasks using image regularization and data reconstruction

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