论文标题
深度卷积转换学习 - 扩展版本
Deep Convolutional Transform Learning -- Extended version
论文作者
论文摘要
这项工作介绍了一种新的无监督表示学习技术，称为“深卷积转换学习”（DCTL）。通过堆叠卷积变换，我们的方法能够在不同层学习一组独立的内核。然后，以无监督的方式提取的功能可用于执行机器学习任务，例如分类和聚类。学习技术依赖于实现的近端最小化方案，并具有既定的融合保证。我们的实验结果表明，所提出的DCTL技术在几个基准数据集上优于其浅版本CTL。
This work introduces a new unsupervised representation learning technique called Deep Convolutional Transform Learning (DCTL). By stacking convolutional transforms, our approach is able to learn a set of independent kernels at different layers. The features extracted in an unsupervised manner can then be used to perform machine learning tasks, such as classification and clustering. The learning technique relies on a well-sounded alternating proximal minimization scheme with established convergence guarantees. Our experimental results show that the proposed DCTL technique outperforms its shallow version CTL, on several benchmark datasets.
