This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well acknowledged. The success of convolutive features owes to the convolutional neural network (CNN). However, CNN cannot perform learning tasks in an unsupervised fashion. In a recent work, we show that such shortcoming can be addressed by adopting a convolutional transform learning (CTL) approach, where convolutional filters are learnt in an unsupervised fashion. The present paper aims at (i) proposing a deep version of CTL, (ii) proposing an unsupervised fusion formulation taking advantage of the proposed deep CTL representation, and (iii) developing a mathematically sounded optimization strategy for performing the learning task. We apply the proposed technique, named DeConFuse, on the problem of stock forecasting and trading. A comparison with state-of-the-art methods (based on CNN and long short-term memory network) shows the superiority of our method for performing a reliable feature extraction.

这项工作提出了一个基于深度卷积变换学习的无监督融合框架。卷积滤波器对数据分析的强大学习能力已广为人知。卷积特征的成功归功于卷积神经网络（CNN）。但是，CNN不能以无人监督的方式执行学习任务。在最近的工作中，我们表明可以通过采用卷积变换学习（CTL）方法解决这种缺点，其中以无监督的方式学习卷积滤波器。本文旨在（i）提出深层的CTL，（ii）提出利用所提出的深层CTL表示的无监督融合公式，以及（iii）开发用于执行学习任务的数学上合理的优化策略。我们应用提出的技术 名为DeConFuse的股票预测和交易问题。与最先进的方法（基于CNN和长短期内存网络）的比较显示了我们执行可靠特征提取方法的优越性。