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Xiaolin Lv - IEEE Xplore Author Profile

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The problem of multi-view unsupervised feature selection receive more attention in recent years. Most of existing works explore the shared information across multi-views. Several embedded algorithms have been developed. All these methods use additional hyper-parameter for regularization, which makes these algorithms are less attractive in practical applications, where it is not easy to select the ...Show More
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 structur...Show More
Facing with the absence of supervised information to guide the search of relevant features and the grid-search of model/hyper-parameters, it is more preferred to develop parameter-free methods and avoid additional hyper-parameters tuning. In this paper, we propose a new simple and effective parameter-free unsupervised feature selection algorithm by minimizing the linear reconstruction weight betwe...Show More