R-PathCluster: Identifying cancer subtype of glioblastoma multiforme using pathway-based restricted boltzmann machine

2017 ◽  
Author(s):  
Tejaswini Mallavarapu ◽  
Youngsoon Kim ◽  
Jung Hun Oh ◽  
Mingon Kang
Keyword(s):  
Glioblastoma Multiforme ◽  
Restricted Boltzmann Machine ◽  
Boltzmann Machine ◽  
Cancer Subtype

Robustness Evaluation of Restricted Boltzmann Machine against Memory and Logic Error

2017 ◽  
Vol E100.C (12) ◽  
pp. 1118-1121 ◽  
Author(s):  
Yasushi FUKUDA ◽  
Zule XU ◽  
Takayuki KAWAHARA
Keyword(s):  
Restricted Boltzmann Machine ◽  
Boltzmann Machine

Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems

2021 ◽  
Vol 17 (11) ◽  
pp. 155014772110553
Author(s):  
Xiaoping Zhou ◽  
Haichao Liu ◽  
Bin Wang ◽  
Qian Zhang ◽  
Yang Wang
Keyword(s):  
Millimeter Wave ◽  
Manifold Learning ◽  
Multiple Input Multiple Output ◽  
Restricted Boltzmann Machine ◽  
Boltzmann Machine ◽  
Hybrid Precoding ◽  
User Clustering ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Low Dimensional

Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The effectiveness of this method in simple systems has been well verified, but its performance is still unknown due to many problems in real communication such as interference from other users and base stations, and users are constantly on the move. In this article, we propose a dynamic user clustering hybrid precoding method in the high-dimensional millimeter-wave multiple-input multiple-output system, which uses low-dimensional manifolds to avoid complicated calculations when there are many antennas. We model each user set as a novel Convolutional Restricted Boltzmann Machine manifold, and the problem is transformed into cluster-oriented multi-manifold learning. The novel Convolutional Restricted Boltzmann Machine manifold learning seeks to learn embedded low-dimensional manifolds through manifold learning in the face of user mobility in clusters. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.

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