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Scalable and Flexible Unsupervised Feature Selection.

Haojie Hu¹, Rong Wang², Xiaojun Yang³

¹Xi'an Research Institute of Hi-Tech, Xi'an 710025, China haojiehu705@gmail.com.

Neural Computation

|January 16, 2019

Summary

This summary is machine-generated.

This study introduces a flexible linearization model using anchor graphs for efficient unsupervised feature selection in large, high-dimensional datasets. The novel approach improves performance and reduces computational demands compared to existing methods.

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Area of Science:

Machine Learning
Data Science
Computer Vision

Background:

Graph-based unsupervised feature selection (GUFS) excels with high-dimensional unlabeled data.
Existing GUFS methods are often slow and require substantial storage for large datasets.
Anchor-based methods accelerate GUFS but can be limited by strict linear constraints.

Purpose of the Study:

To develop a more efficient and effective anchor-based graph learning model for feature selection.
To address the limitations of time-consumption and large storage in existing GUFS algorithms.
To improve the performance of anchor-based methods by introducing a flexible linearization model.

Main Methods:

Proposed a flexible linearization model incorporating an anchor graph and L-norm regularization.
Constructed an anchor-based graph Laplacian using a parameter-free adaptive neighbor assignment strategy.
Developed an efficient iterative algorithm to solve the optimization problem and proved its convergence.

Main Results:

The proposed method effectively handles large-scale datasets.
Demonstrated improved performance compared to existing anchor-based feature selection techniques.
Experiments confirmed the method's effectiveness and efficiency on public datasets.

Conclusions:

The novel flexible linearization model with anchor graph offers a significant advancement in GUFS.
The proposed approach provides an efficient and scalable solution for high-dimensional feature selection.
This method enhances manifold embedding structure characterization and optimizes computational performance.