Hierarchical Feature Selection for Random Projection
Hierarchical Feature Selection for Random Projection
复制标题
DOI：
10.1109/tnnls.2018.2868836
复制
发表时间：
2019-05
影响因子：
10.4
通讯作者：
Qi Wang;Jia Wan;F. Nie;Bo Liu;C. Yan;Xuelong Li
中科院分区：
文献类型：
--
作者：
Qi Wang;Jia Wan;F. Nie;Bo Liu;C. Yan;Xuelong Li
随机投影是一种流行的机器学习算法，可以通过神经网络实现，并以非常有效的方式进行训练。然而，当应用于相当大规模的数据集时，特征的数量应该足够大，这导致测试过程的速度较慢，并且在某些情况下需要更多的存储空间。此外，一些特征是冗余的，甚至是噪声，因为它们是随机生成的，所以性能可能会受到这些特征的影响。为了解决这些问题，引入了一种有效的特征选择方法来分层选择有用的特征。提出了一种新的神经元选择准则，为神经网络结构设计提供了新的思路。与传统方法相比，该方法在分类和回归任务上的测试时间和精度都有所提高。大量的实验证实了所提出的方法的有效性。
Random projection is a popular machine learning algorithm, which can be implemented by neural networks and trained in a very efficient manner. However, the number of features should be large enough when applied to a rather large-scale data set, which results in slow speed in testing procedure and more storage space under some circumstances. Furthermore, some of the features are redundant and even noisy since they are randomly generated, so the performance may be affected by these features. To remedy these problems, an effective feature selection method is introduced to select useful features hierarchically. Specifically, a novel criterion is proposed to select useful neurons for neural networks, which establishes a new way for network architecture design. The testing time and accuracy of the proposed method are improved compared with traditional methods and some variations on both classification and regression tasks. Extensive experiments confirm the effectiveness of the proposed method.