论文标题
稀疏数据到结构化图像集转换
Sparse data to structured imageset transformation
论文作者
论文摘要
如果样本和功能的数量非常大，涉及稀疏数据集的机器学习问题可能会受益于使用卷积神经网络。在各种不同的域中，这种数据集越来越频繁地遇到。我们在尝试提供可与卷积神经网络一起使用的每个图像结构的同时将这些数据集转换为图像集。两个公开可用的稀疏数据集的实验结果表明，与其他方法相比，该方法可以提高分类性能，这可能归因于在结果图像上形成可视上可区分的形状。
Machine learning problems involving sparse datasets may benefit from the use of convolutional neural networks if the numbers of samples and features are very large. Such datasets are increasingly more frequently encountered in a variety of different domains. We convert such datasets to imagesets while attempting to give each image structure that is amenable for use with convolutional neural networks. Experimental results on two publicly available, sparse datasets show that the approach can boost classification performance compared to other methods, which may be attributed to the formation of visually distinguishable shapes on the resultant images.
