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Statistical 3D watermarking algorithm using non negative matrix factorization

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第35-36期79卷 25889-25904页

作者： Medimegh, Nassima Belaid, Samir Atri, Mohamed Werghi, Naoufel Univ Monastir Fac Sci Microelect Lab Monastir Tunisia Univ Sousse Higher Inst Comp Sci & Telecom ISITCom MARS Res Lab LR17ES05 Sousse Tunisia Khalifa Univ Dept Elect & Comp Engn Abu Dhabi U Arab Emirates

In this paper, we propose a robust blind watermarking method for 3D object based on non negative matrix factorization (NMF). Our main idea is to use the NMF basis matrix guide the watermark embedding. We first segment the model into partitions based on its salient points. Then, we apply NMF algorithm to the vertex norms. The distribution of vertex is grouped into bins according to the basis matrixX. Finally, we insert watermark bits in the vertex norm distribution. Experimental results demonstrate that the proposed method achieved good visual quality and also robustness against various attacks.

关键词： 3D watermarking Statistical method non negative matrix factorization

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Divergence Functions of non negative matrix factorization: A Comparison Study

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2011年第10期40卷 1594-1612页

作者： Zhang, Zhong-Yuan Cent Univ Finance & Econ Sch Stat Beijing Peoples R China

non negative matrix factorization (NMF) has become one of the most popular models in data mining for its good performance in unsupervised learning applications. Recently, a variety of divergence functions have been extensively studied for NMF. But, there is still lack of analysis on the relationships between the divergence functions and the applications. This article tries to give some preliminary results on this interesting problem. Our experiments show that the most familiar two divergence functions-the least squares error and the K-L divergence-are competent for unsupervised learning such as gene expression data clustering and image processing.

关键词： Blind source separation Comparison Clustering Divergence function Image processing non negative matrix factorization

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SPEECH ENHANCEMENT USING BEAMFORMING AND non negative matrix factorization FOR ROBUST SPEECH RECOGNITION IN THE CHIME-3 CHALLENGE

SPEECH ENHANCEMENT USING BEAMFORMING AND NON NEGATIVE MATRIX...

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IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

作者： Vu, Thanh T. Bigot, Benjamin Chng, Eng Siong Nanyang Technol Univ Rolls Royce NTU Corp Lab Singapore Singapore

ISBN: (纸本)9781479972913

In this paper we present our contribution to the third CHiME challenge on speech separation and recognition for noisy multi-channel recordings. The use-case of the challenge consists in single speaker utterances recorded in highly non-stationary noisy environments using a 6-microphone array mounted on a tablet computer. The front-end of our system is performing speech enhancement by cascading a cross-correlation-based channel selection, Signal Dependent MVDR beamforming and online source separation based on sparse NMF. The back-end module is a state-of-the-art speech recognition system with DNN acoustic models trained on fMLLR features and a RNN Language Model. Our system reaches an overall WER of 11.9 4 % on real test recordings, achieving a relative improvement of 65 % compared to the baseline system.

关键词： Speech Enhancement Automatic Speech Recognition MVDR Beamforming non negative matrix factorization CHiME challenge

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SEMI-SUPERVISED VISUAL RECOGNITION WITH CONSTRAINED GRAPH REGULARIZED non negative matrix factorization

SEMI-SUPERVISED VISUAL RECOGNITION WITH CONSTRAINED GRAPH RE...

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20th IEEE International Conference on Image Processing (ICIP)

作者： Guo, Weiwei Hu, Weidong Boulgouris, Nikolaos V. Patras, Ioannis Natl Univ Def & Technol Changsha Hunan Peoples R China Brunel Univ Uxbridge UB8 3PH Middx England Univ London London WC1E 7HU England

ISBN: (纸本)9781479923410

This paper proposes a semi-supervised nonnegative matrix factorization algorithm for face and gait recognition. The proposed algorithm imposes hard constraints on the labelled data points, such that the data points that belong to the same class are projected to the same lower dimensional point. In addition, it introduces a graph Laplacian regularization term that preserves the local geometry structure of the data by penalising large distances between the projections of points that are close in the original space. This results in a constrained optimization problem, that is solved using block coordinate descent with multiplicative update rules. Experimental results on several publicly available datasets demonstrate that proposed method performs in par or considerably better than state of the art methods.

关键词： non negative matrix factorization Object Recognition Semi-supervised Learning

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Solution Space of non negative matrix factorization and Consideration of Feature Extraction on Web Application Attacks

Solution Space of Non Negative Matrix Factorization and Cons...

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International Conference on Information Science, Electronics and Electrical Engineering (ISEEE)

作者： Matsuda, Takeshi Shizuoka Inst Sci & Technol Dept Comp Sci Fukuroi Shizuoka 4378555 Japan

non negative matrix factorization (NMF) has been used for multivariate data analysis, and a lot of algorithms concerning NMF have been investigated. In this paper, we consider the solution space of NMF from the standp... 详细信息

ISBN: (纸本)9781479931972

关键词： non negative matrix factorization Grobner basis Solution Space Feature Extraction Web Application Attack

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URL Classification Using non negative matrix factorization

URL Classification Using Non Negative Matrix Factorization

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20th National Conference on Communications (NCC)

作者： Khare, Shreya Bhandari, Akshay Murthy, Hema A. Indian Inst Technol Dept Comp Sci & Engn Madras 600036 Tamil Nadu India

ISBN: (纸本)9781479923618

Internet availability on a campus is not metered. Internet link bandwidths are vulnerable as they can be misused. Moreover, websites blacklist campuses for misuse. Especially blacklisting by academic websites like IEEE and ACM can lead to serious researchers being denied access to information. The objective of this paper is to proactively classify anomalous accesses. This will enable campus ISPs to deny access to users, misusing the Internet. In particular URLs are classified using the short snippets(meta-data) that are available. New Features, namely random walk term weights, within class popularity in tandem with non negative matrix factorization show a lot of promise for classifying URLs. The classification accuracy is as a high as 92.96% on 10 gigabytes of proxy data.

关键词： Web Page Classification non negative matrix factorization

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A New Algorithm for Solving Data Sparsity problem Based-On non negative matrix factorization in Recommender Systems 4

A New Algorithm for Solving Data Sparsity problem Based-On N...

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4th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Sharifi, Zeinab Rezghi, Mansoor Nasiri, Mahdi Islamic Azad Univ Dept Comp Engn Sci & Res Branch Guilan Iran Univ Tarbiat Modares Dept Comp Sci Tehran Iran Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781479954865

The "sparsity" challenge is a well-known problem in recommender systems. This issue relates to little information about each user or item in large data set. The purpose of this paper is pre-processing of data and using non negative matrix factorization (NMF) method to improve this challenge. Since the original data are non negative, the algorithm based on NMF maintains positive effect of data on decomposition matrices and makes better prediction of original data in comparison to singular value decomposition (SVD) algorithm. Since the dimensions of data are very large, it offers a solution based on dimensionality reduction in which useful factors for selecting optimal dimensions (optimal 'k') are extracted from the data matrix until appropriate approximation of the original data obtains from rank 'k' matrices. Thus, the presented model not only selects the best factors from the original data but also, recommends appropriate values for the missing ratings and overcome sparsity problem. The results of experiments are evaluated with three metrics: RMSE1, NMAE(2), and RE3. Results show that our approach leads to better prediction.

关键词： Recommender System non negative matrix factorization Collaborative Filtering Data Sparsity

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Separation of human and animal seismic signatures using non-negative matrix factorization

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PATTERN RECOGNITION LETTERS 2012年第16期33卷 2085-2093页

作者： Mehmood, Asif Damarla, Thyagaraju Sabatier, James USA Res Lab Adelphi MD 20783 USA

Seismic footstep detection based systems can be employed for homeland security applications such as perimeter protection and the border security. This paper reports an approach based on non-negative matrix factorization (NMF) for seismic footstep signal separation for a single channel recording. A supervised NMF technique is employed to separate the human footstep signatures from the horse footstep signatures. The proposed algorithm is applied on the spectrogram of human footstep signals and horse footstep signals. The spectrograms of these signals are presented as a sum of components, each having a fixed spectrum and time-varying gain. The main benefit of the proposed technique is its ability to decompose a complex signal automatically into objects that have a meaningful interpretation. In this paper, a sparsity-based NMF algorithm is developed and implemented on seismic data of human and horse footsteps. The performance of this method is very promising and is demonstrated by the experimental results. (c) 2012 Elsevier B.V. All rights reserved.

关键词： non negative matrix factorization Dimensionality reduction Sparsity Single channel source separation Spectrogram

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Discovering Perturbation of Modular Structure in HIV Progression by Integrating Multiple Data Sources Through non-negative matrix factorization

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018年第3期15卷 869-877页

作者： Ray, Sumanta Maulik, Ujjwal Aliah Univ Dept Comp Sci & Engn Kolkata 700156 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India

Detecting perturbation in modular structure during HIV-1 disease progression is an important step to understand stage specific infection pattern of HIV-1 virus in human cell. In this article, we proposed a novel methodology on integration of multiple biological information to identify such disruption in human gene module during different stages of HIV-1 infection. We integrate three different biological information: gene expression information, protein-protein interaction information, and gene ontology information in single gene meta-module, through non negative matrix factorization (NMF). As the identified meta-modules inherit those information so, detecting perturbation of these, reflects the changes in expression pattern, in PPI structure and in functional similarity of genes during the infection progression. To integrate modules of different data sources into strong meta-modules, NMF based clustering is utilized here. Perturbation in meta-modular structure is identified by investigating the topological and intramodular properties and putting rank to those meta-modules using a rank aggregation algorithm. We have also analyzed the preservation structure of significant GO terms in which the human proteins of the meta-modules participate. Moreover, we have performed an analysis to show the change of coregulation pattern of identified transcription factors (TFs) over the HIV progression stages.

关键词： non negative matrix factorization data integration gene expression protei-protein interaction

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SEMI-SUPERVISED VISUAL RECOGNITION WITH CONSTRAINED GRAPH REGULARIZED non negative matrix factorization

SEMI-SUPERVISED VISUAL RECOGNITION WITH CONSTRAINED GRAPH RE...

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IEEE International Conference on Image Processing

作者： Weiwei Guo Weidong Hu Nikolaos V. Boulgouris Ioannis Patras National University of Defense and Technology Brunel University Queen Mary University of London

ISBN: (纸本)9781479923427

关键词： non negative matrix factorization Object Recognition Semi-supervised Learning object recognition matrix decomposition state of the art Semi-supervised Learning factorization NOTIFICATION OF non-COMPLIANCE Visualization negative Constrained optimization

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