Unsupervised feature selection method based on hidden space learning and popular constraint

Abstract

The invention discloses an unsupervised feature selection method based on hidden space learning and popularity constraint, which comprises the following steps: S11, inputting an original data matrix to obtain a feature selection model; s12, embedding hidden space learning into the feature selection model to obtain a feature selection model with hidden space learning; s13, adding a graph Laplacian regularization item into the feature selection model with hidden space learning to obtain a target function; s14, solving the objective function by adopting an alternating iterative optimization strategy; and S15, sorting each feature in the original matrix, and selecting the features ranking the top k to obtain an optimal feature subset. According to the method, feature selection is carried out in a learned potential hidden space, and the space is robust to noise; the potential hidden space is modeled by non-negative matrix factorization of similar matrices, which matrix factorization can explicitly reflect relationships between data instances. Meanwhile, a local manifold structure of an original data space is reserved by a graph-based manifold constraint term in a potential hidden space.

Description

technical field [0001] The invention relates to the technical fields of signal processing and data analysis, in particular to an unsupervised feature selection method based on latent space learning and popular constraints. Background technique [0002] With the advent of the era of information explosion, a large amount of high-dimensional data is generated, such as images, texts, and medical microarrays. Directly processing these high-dimensional data will not only significantly increase the computational time and memory burden of algorithms and computer hardware, but also lead to poor performance due to the existence of irrelevance, noise, and redundant dimensions. The intrinsic dimension of high-dimensional data is usually small, and only a part of features can be used to complete the task. As an effective preprocessing of high-dimensional data, feature selection aims to achieve dimensionality reduction by removing some irrelevant and redundant features while preserving t...

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Problems solved by technology

Second, most previous methods perform feature selection in the raw data space, an

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