scholarly journals Some Issues in Computing the CP Decomposition of NonNegative Tensors

2018 ◽  
pp. 57-66 ◽  
Author(s):  
Mohamad Jouni ◽  
Mauro Dalla Mura ◽  
Pierre Comon
Keyword(s):  
Nonnegative Tensors ◽  
Cp Decomposition

scholarly journals Some inequalities on the spectral radius of nonnegative tensors

2020 ◽  
Vol 18 (1) ◽  
pp. 262-269
Author(s):  
Chao Ma ◽  
Hao Liang ◽  
Qimiao Xie ◽  
Pengcheng Wang
Keyword(s):  
Spectral Radius ◽  
Nonnegative Tensors

Abstract The eigenvalues and the spectral radius of nonnegative tensors have been extensively studied in recent years. In this paper, we investigate the analytic properties of nonnegative tensors and give some inequalities on the spectral radius.

Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Jingjing Wang ◽  
Wenjun Jiang ◽  
Kenli Li ◽  
Keqin Li
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Prediction Error ◽  
High Efficiency ◽  
Optimization Problems ◽  
Online Social Network ◽  
Reconstruction Error ◽  
Incremental Updating ◽  
Cp Decomposition ◽  
Error Accumulation

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.

scholarly journals Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangwen Liao ◽  
Lingying Zhang ◽  
Jingjing Wei ◽  
Dingda Yang ◽  
Guolong Chen
Keyword(s):  
Network Centrality ◽  
Network Clustering ◽  
Tensor Factorization ◽  
Latent Factors ◽  
Cp Decomposition ◽  
Temporal Features ◽  
User Influence ◽  
Proposed Model ◽  
The Mean ◽  
Content Similarity

User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user’s temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user’s opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

scholarly journals Fast Decomposition of Large Nonnegative Tensors

2015 ◽  
Vol 22 (7) ◽  
pp. 862-866 ◽  
Author(s):  
Jeremy E. Cohen ◽  
Rodrigo Cabral Farias ◽  
Pierre Comon
Keyword(s):  
Nonnegative Tensors ◽  
Fast Decomposition

scholarly journals Robust low-rank tensor modelling using Tucker and CP decomposition

2017 ◽  
Author(s):  
Niannan Xue ◽  
George Papamakarios ◽  
Mehdi Bahri ◽  
Yannis Panagakis ◽  
Stefanos Zafeiriou
Keyword(s):  
Low Rank ◽  
Rank Tensor ◽  
Cp Decomposition

Photocatalytic oxidation of chlorophenols in the presence of manganese ions

1999 ◽  
Vol 39 (10-11) ◽  
pp. 225-230 ◽  
Author(s):  
Jong-Nan Chen ◽  
Yi-Chin Chan ◽  
Ming-Chun Lu
Keyword(s):  
Aqueous Solution ◽  
Ionic Strength ◽  
Photocatalytic Oxidation ◽  
Reaction Rate ◽  
Removal Rate ◽  
Adsorption Rate ◽  
Manganese Ions ◽  
Manganese Ion ◽  
Cp Decomposition ◽  
The Relationship

The decomposition of chlorophenols in aqueous solution with UV-illuminated TiO2 suspensions in the presence of manganese ions was studied. It was found that the removal rate of chlorophenols would be the highest at pH 3 in the presence of 1.18×10−4 M manganese ion. The effect of ionic strength on the 2-CP decomposition can be ignored in the range from 0.1 to 0.005 M for NaClO4. This study is also to explore the relationship between the adsorption rate and reaction rate. Results showed that the more the adsorption rate the more the decomposition rate for the three chlorophenols. Manganese ions can increase the photocatalytic oxidation of 2-chlorophenol in terms of DOC. The relationship between temperature and reaction rate for 2-CP is k = 0.0043T - 1.2146.

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