A Filter-based Unsupervised Feature Selection Method via Improved Local Structure Preserving | IEEE Conference Publication | IEEE Xplore

A Filter-based Unsupervised Feature Selection Method via Improved Local Structure Preserving


Abstract:

In this paper, we propose a novel filter based unsupervised feature selection algorithm. We first extract the global level manifold structure using LLE on all features. W...Show More

Abstract:

In this paper, we propose a novel filter based unsupervised feature selection algorithm. We first extract the global level manifold structure using LLE on all features. We also extract the feature level manifold structure using LLE on each single feature. We then compute the feature-wise non-negative local linear reconstruction weight to capture the feature relationship. The true manifold structure of feature is then computed by the linear combination of its own Laplacian matrix and its neighbor's Laplacian matrices. The importance of feature is then evaluated by the difference between the global manifold structure and the combined feature level manifold structure. Extensive experimental results on benchmark data sets well demonstrate that the proposed method outperform state-of-the-art filter-based unsupervised feature selection methods.
Date of Conference: 08-10 July 2019
Date Added to IEEE Xplore: 19 August 2019
ISBN Information:
Conference Location: Kunming, China

I. Introduction

Data in real world applications usually have huge amount and high dimensionality. These data often contain irrelevant, noisy and redundant features. These features present great challenging for data storage, computation and the curse of dimensionality. To remedy these limitations, feature selection techniques have been developed in the last decayed to keep only a few relevant and informative features. Given the selected features, the subsequent learning process could be accelerated, and even the generalization ability could be improved.

References

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