稀疏数据到结构化图像的转换
Sparse data to structured imageset transformation
摘要：
如果样本和特征的数量非常大，那么涉及稀疏数据集的机器学习问题可能会受益于卷积神经网络的使用。这种数据集在各种不同的领域中越来越频繁地遇到。我们将这些数据集转换为图像集，同时尝试为每个图像结构提供适合用于卷积神经网络的图像结构。在两个公开的稀疏数据集上的实验结果表明，与其他方法相比，该方法可以提高分类性能，这可能归因于在所得图像上形成视觉上可区分的形状。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
