文章基本信息

标题：DeConFuse: a deep convolutional transform-based unsupervised fusion framework
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作者：Pooja Gupta ; Jyoti Maggu ; Angshul Majumdar 等
期刊名称：EURASIP Journal on Advances in Signal Processing
印刷版ISSN：1687-6172
电子版ISSN：1687-6180
出版年度：2020
卷号：2020
期号：1
页码：1
DOI：10.1186/s13634-020-00684-5
出版社：Hindawi Publishing Corporation
摘要：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.
关键词：Information fusion ; Deep learning ; Convolution ; Stock trading ; Financial forecasting