http://swrc.ontoware.org/ontology#TechnicalReport
Sequential semi-orthogonal multi-level NMF with negative residual reduction for network embedding
en
電気通信大学情報理工学域
早稲田大学基幹理工学部情報通信学科
橋本 陸
笠井 裕之
Network embedding is intended to produce low-dimensional vector representations of nodes in a network to preserve and extract the latent network structure, which has higher robustness to noise, outliers, and redundant data. Although a recently proposed multi-level nonnegative matrix factorization (NMF)-based approach has exhibited superior performance on network analysis, it is adversely affected by performance degradation because of discarded negative residual and redundant base selection throughout sequential multiple factorization processes. To alleviate this shortcoming, this paper presents a proposal of a sequential semi-orthogonal NMF with negative residual reduction for the network embedding (SSO-NRR-NMF). The proposed approach reduces the negative residuals to be discarded, and avoids redundant bases with a semi-orthogonal constraint.
Network embedding is intended to produce low-dimensional vector representations of nodes in a network to preserve and extract the latent network structure, which has higher robustness to noise, outliers, and redundant data. Although a recently proposed multi-level nonnegative matrix factorization (NMF)-based approach has exhibited superior performance on network analysis, it is adversely affected by performance degradation because of discarded negative residual and redundant base selection throughout sequential multiple factorization processes. To alleviate this shortcoming, this paper presents a proposal of a sequential semi-orthogonal NMF with negative residual reduction for the network embedding (SSO-NRR-NMF). The proposed approach reduces the negative residuals to be discarded, and avoids redundant bases with a semi-orthogonal constraint.
AN10438399
研究報告オーディオビジュアル複合情報処理（AVM）
2019-AVM-107
21
1-3
2019-11-28
2188-8582