Generalized Centered 2-D Principal Component Analysis

Zhou, Ge; Xu, Guangquan; Hao, Jianye; Chen, Shizhan; Xu, Jun; Zheng, Xi

doi:10.1109/tcyb.2019.2931957

Cited by 24 publications

(13 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quality of purified image is still within an acceptable range, and the document image as secret message achieves a certain removal effect. However, we only explore the lightweight deep-learning steganography removal model from the perspective of pruning, so our future work will focus on the following aspects: (1) Explore a fast adaptive structure adjustment algorithm (2) Explore the feasibility of deep-learning steganography removal based on knowledge distillation [47] and quantification (3) We plan to use dimensionality reduction technology [48] to document images to improve model performance.…”

Section: Discussionmentioning

confidence: 99%

Deep-Learning Steganalysis for Removing Document Images on the Basis of Geometric Median Pruning

et al. 2020

View full text Add to dashboard Cite

The deep-learning steganography of current hotspots can conceal an image secret message in a cover image of the same size. While the steganography secret message is primarily removed via active steganalysis. The document image as the secret message in deep-learning steganography can deliver a considerable amount of effective information in a secret communication process. This study builds and implements deep-learning steganography removal models of document image secret messages based on the idea of adversarial perturbation removal: feed-forward denoising convolutional neural networks (DnCNN) and high-level representation guided denoiser (HGD). Further—considering the large computation cost and storage overheads of the above model—we use the document image-quality assessment (DIQA) as threshold, calculate the importance of filters using geometric median and prune redundant filters as extensively as possible through the overall iterative pruning and artificial bee colony (ABC) automatic pruning algorithms to reduce the size of the network structure of the existing vast and over-parameterized deep-learning steganography removal model, while maintaining the good removal effects of the model in the pruning process. Experiment results showed that the model generated by this method has better adaptability and scalability. Compared with the original deep-learning steganography removal model without pruning in this paper, the classic indicators params and flops are reduced by more than 75%.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep-Learning Steganalysis for Removing Document Images on the Basis of Geometric Median Pruning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Massa and Ansari used explicit trust as a measure of user similarity that allows trust to be spread over a trust network. The propagation of trust greatly relieves the sparsity of trust matrix and improves the accuracy of the recommendation system [39], [40]. Golbeck proposed a framework based on trust propagation to predict the rating for the target project [41].…”

Section: Related Workmentioning

confidence: 99%

Sparse Trust Data Mining

Nie

Jiao

et al. 2021

IEEE Trans.Inform.Forensic Secur.

Self Cite

View full text Add to dashboard Cite

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem , which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

show abstract

“…Therefore, we need regularization processing to improve the diversity of summation weight vectors across different attention-hops. We adopt two different regularizers [32] to improve the diversity of attention mechanisms.…”

Section: ) Regularization Mechanism For Self-attentionmentioning

confidence: 99%

Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression

Zhou

Song

et al. 2020

IEEE Access

View full text Add to dashboard Cite

It is non-trivial to predict the prices of precious metals since a number of factors can affect the fluctuations of precious metal prices. Either parametric models or machine learning models cannot accurately forecast the precious metal prices. Though deep learning approaches show their strengths in extracting key features from complicated data, they have the limitations of learning localization and losing some temporal and spatial features. The recent advances in attention mechanisms bring the opportunities to overcome the limitation of deep learning models. In this paper, we originally propose a Regularization Self-Attention Regression Model for precious metal price prediction. In particular, the proposed RSAR model consists of convolutional neural network (CNN) component and Long Short-Term Memory Neural Networks (LSTM) component. Integrating with self-attention mechanism, this model can extract both spatial and temporal features from precious metal price data. Meanwhile, the proper configuration of regularization functions can also lead to the further performance improvement. Extensive experiments on realistic precious metal price dataset show that our proposed approach outperforms other conventional machine learning and deep learning methods. INDEX TERMS Long short-term memory, convolutional neural network, attention mechanism, financial data analysis, deep learning.

show abstract

Generalized Centered 2-D Principal Component Analysis

Cited by 24 publications

References 26 publications

Deep-Learning Steganalysis for Removing Document Images on the Basis of Geometric Median Pruning

Deep-Learning Steganalysis for Removing Document Images on the Basis of Geometric Median Pruning

Sparse Trust Data Mining

Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression

Contact Info

Product

Resources

About