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DeConFuse: a deep convolutional transform-based unsupervised fusion framework

Authors :
Pooja Gupta
Jyoti Maggu
Angshul Majumdar
Emilie Chouzenoux
Giovanni Chierchia
Source :
EURASIP Journal on Advances in Signal Processing, Vol 2020, Iss 1, Pp 1-32 (2020)
Publication Year :
2020
Publisher :
SpringerOpen, 2020.

Abstract

Abstract 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.

Details

Language :
English
ISSN :
16876180
Volume :
2020
Issue :
1
Database :
Directory of Open Access Journals
Journal :
EURASIP Journal on Advances in Signal Processing
Publication Type :
Academic Journal
Accession number :
edsdoj.391082765994db5997e065f99108da6
Document Type :
article
Full Text :
https://doi.org/10.1186/s13634-020-00684-5