Subspace learning-based graph regularized feature selection
نویسندگان
چکیده
منابع مشابه
Self-representation based dual-graph regularized feature selection clustering
Feature selection algorithms eliminate irrelevant and redundant features, even the noise, while preserving the most representative features. They can reduce the dimension of the dataset, extract essential features in high dimensional data and improve learning quality. Existing feature selection algorithms are all carried out in data space. However, the information of feature space cannot be ful...
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Recent research has shown the benefits of large margin framework for feature selection. In this paper, we propose a novel feature selection algorithm, termed as Large Margin Subspace Learning (LMSL), which seeks a projection matrix to maximize the margin of a given sample, defined as the distance between the nearest missing (the nearest neighbor with the different label) and the nearest hit (th...
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Dimensionality reduction is a very important topic in machine learning. It can be generally classified into two categories: feature selection and subspace learning. In the past decades, many methods have been proposed for dimensionality reduction. However, most of these works study feature selection and subspace learning independently. In this paper, we present a framework for joint feature sel...
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Sparse coding has gained popularity and interest due to the benefits of dealing with sparse data, mainly space and time efficiencies. It presents itself as an optimization problem with penalties to ensure sparsity. While this approach has been studied in the literature, it has rarely been explored within the confines of clustering data. It is our belief that graph-regularized sparse coding can ...
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Clustering is the process which is used to assign a set of n objects into clusters(groups). Dimensionality reduction techniques help in increasing the accuracy of clustering results by removing redundant and irrelevant dimensions. But, in most of the situations, objects can be related in different ways in different subsets of the dimensions. Dimensionality reduction tends to get rid of such rel...
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ژورنال
عنوان ژورنال: Knowledge-Based Systems
سال: 2016
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2016.09.006