Please use this identifier to cite or link to this item:
Title: Super-resolution for sequence series data using long-short term memory network
Authors: Wong, Pak-Kan 
Wong, Man-Leung 
Prof. LEUNG Kwong Sak 
Issue Date: 2017
Publisher: IEEE
Source: 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings 2018-January, pp. 1-8
Conference: 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings 2018-January 
Abstract: Sequence Series Data (SSD) refers to multi-dimensional data involving measurements over sequences, which can be ordered. This type of data is frequently encountered in genomic data sets and text sentiment analysis data sets, but collecting them can be time-consuming and labour-intensive. These factors result in low-resolution data sets. Therefore, we employed six machine learning regression methods to perform SSD super-resolution, i.e. to recover high-resolution data sets using self-similarity in low-resolution data sets. Furthermore, we propose a novel Long-Short Term Memory (LSTM) network, namely Interaction Encoded LSTM (IELSTM) network, which is capable of handling multiple distant interactions among sequences. IELSTM network generally shows better overall reconstruction quality when compared with ridge regression, LASSO regression, orthogonal matching pursuit regression, multilayer perceptron regression, and random forest regression, on four genomic data sets.
Type: Conference Paper
DOI: 10.1109/SSCI.2017.8280957
Appears in Collections:Publication

Show full item record

Page view(s)

checked on Sep 14, 2023

Google ScholarTM

Impact Indices




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.