School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, China.
School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, China.
Neural Netw. 2015 Sep;69:126-34. doi: 10.1016/j.neunet.2015.06.001. Epub 2015 Jun 15.
Locality preserving measurement criterion is frequently used for assessing the quality of features. However, locality preserving criterion based unsupervised feature selection algorithms have two widely acknowledged weaknesses: (1) The performance of feature selection heavily depends on the effectiveness of the similarity matrix, which is defined in the original space, and thus it is probably inconsistent with the one in the weighted space. (2) Greedy searching strategy neglects the correlation and redundancy among features. To alleviate these deficiencies, we propose a novel unsupervised feature selection algorithm by jointly learning adaptive nearest neighbors in the weighed space. An effective iterative algorithm is developed to solve the proposed formulation, where each iteration reduces to a convex subproblem which can be efficiently solved with some off-the-shelf toolboxes. The results of experiments on the UCI and face data sets demonstrate the effectiveness of the proposed algorithm, for outperforming many state-of-the-art unsupervised and supervised feature selection methods in terms of classification accuracy.
局部保持度量准则常用于评估特征的质量。然而，基于局部保持准则的无监督特征选择算法有两个公认的缺点：（1）特征选择的性能严重依赖于原始空间中定义的相似性矩阵的有效性，因此它可能与加权空间中的不一致。（2）贪婪搜索策略忽略了特征之间的相关性和冗余性。为了缓解这些不足，我们提出了一种新的无监督特征选择算法，通过联合学习加权空间中的自适应最近邻。开发了一种有效的迭代算法来求解所提出的公式，其中每个迭代都简化为一个凸子问题，可以使用一些现成的工具箱有效地解决。在 UCI 和人脸数据集上的实验结果表明了所提出算法的有效性，在分类准确性方面优于许多最先进的无监督和监督特征选择方法。
