  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 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; (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.
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.
