On Accelerating Multi-Layered Heterogeneous Network Representation Learning via Landmark Selection
碩士 === 國立交通大學 === 電機工程學系 === 106 === Network representation, embedding large information networks into low dimensional vector spaces, has been widely studied in homogeneous networks. Deriving the latent representations of the information networks can apply to data analysis methods such as visualizin...
Main Authors: | Tsai, Cheng-Ming, 蔡政銘 |
---|---|
Other Authors: | 帥宏翰 |
Format: | Others |
Language: | en_US |
Published: |
2017
|
Online Access: | http://ndltd.ncl.edu.tw/handle/4fm32b |
Similar Items
-
Enhancing Both Efficiency and Representational Capability of Isomap by Extensive Landmark Selection
by: Dong Liang, et al.
Published: (2015-01-01) -
Multi-layer Tree Structured Model with Sparse Coded Clustering for Facial Landmark Localization
by: Yu-Ming Kuo, et al.
Published: (2016) -
Representation of visual landmarks in retrosplenial cortex
by: Lukas F Fischer, et al.
Published: (2020-03-01) -
Visual EKF-SLAM from Heterogeneous Landmarks
by: Jorge Othón Esparza-Jiménez, et al.
Published: (2016-04-01) -
Facial Landmark Detection via Attention-Adaptive Deep Network
by: Muhammad Sadiq, et al.
Published: (2019-01-01)