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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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Multi -View Clustering Using Sparse non-negative matrix factorization for Recommendation Systems 22

Multi -View Clustering Using Sparse Non-Negative Matrix Fact...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Bachiri, Khalil Boufares, Faouzi Malek, Maria Rogovschi, Nicoleta Yahyaouy, Ali Cy Cergy Paris University Lisac Laboratory Sidi Mohamed Ben Abdellah University la Maison des Sciences Numeriques Etis Laboratory Cergy France University Sorbonne Paris Nord Laboratory LIPN-UMR 7030-CNRS la Maison des Sciences Numeriques Villetaneuse France Cy Cergy Paris University Etis Laboratory Cergy France University of Paris Descartes Lipade Laboratory Paris France Sidi Mohamed Ben Abdellah University Lisac Laboratory Fez Morocco

ISBN: (纸本)9798350345346

Real-world datasets often comprise multiple views or representations, such as user profiles, comments, and preferences. Integrating information from these multi-view datasets can significantly enhance recommendation performance. Consequently, several multi-view clustering (MVC) approaches have been proposed and extensively applied to exploit the compatibility of diverse data sources. Among these methods, non-negative matrix factorization (NMF) has garnered significant attention due to its ability to process high-dimensional data. However, most NMF-based MVC methods suffer from two critical drawbacks: the similarity matrices constructed using traditional methods fail to fully exploit neighborhood information among data points, and they are highly sensitive to noise, missing values, and outliers. Additionally, existing NMF-based clustering methods often do not generate sparse coefficient matrices, leading to suboptimal data representation based on the basis matrix. To address these challenges and enhance the accuracy and efficiency of recommendation algorithms, we propose a novel approach called Multi-View Clustering based on Sparse non-negative matrix factorization (MVC-SNMF). This method leverages the manifold structure of multi-view data and incorporates a sparsity constraint to effectively handle noise while preserving the geometric structure. Furthermore, we introduce a sparsity constraint on the rows of the coefficient matrix to enhance the visibility of clustering structures. To efficiently solve the proposed non-convex optimization problem, we devise an iterative update scheme. Our experimental results on real datasets from *** and Yelp demonstrate the effectiveness of our approach in improving clustering performance, surpassing most existing clustering algorithms. The proposed method not only enhances the clustering accuracy but also exhibits robustness to noise and missing values, making it a promising solution for practical recommendation systems. © 202

关键词： Graph regularized and Sparse distance graph Multi view clustering non negative matrix factorization Recommendation system

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ROBUST COUPLED non-negative matrix factorization FOR HYPERSPECTRAL AND MULTISPECTRAL DATA FUSION

ROBUST COUPLED NON-NEGATIVE MATRIX FACTORIZATION FOR HYPERSP...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ahmad, Touseef Lyngdoh, Rosly B. Anand, S. S. Gupta, Praveen K. Misra, Arundhati Raha, Soumyendu ISRO Space Applicat Ctr Ahmadabad 380015 Gujarat India Indian Inst Sci Bangalore Bangalore 560012 Karnataka India

ISBN: (纸本)9781665403696

In recent time, Hyperspectral(HS) and multispectral(MS) data fusion based on spectral unmixing methods has become an active area of research. Coupled non-negative matrix factorization (CNMF), one among many unmixing-based data fusion approaches, performs alternating unmixing of the HS and MS data while connecting the results by point spread function and spectral response function of the sensors. However, CNMF operates exclusively on the spectral information of the HS and MS data and also disregards the spatial distribution of the data. In this paper, we propose an extended linear mixing model approach to enhance the spatial resolution of HS data. The proposed method extends the commonly used linear mixing model for data fusion by introducing an additional term that accounts for the non-linearity effects as well. The results of the simulation obtained from the analysis on various synthesized datasets suggest that the proposed method can significantly improve the Peak signal-to-noise ratio (PSNR), minimize the relative dimensionless global error (ERGAS) of fusion, and also competes with state-of-the-art approaches.

关键词： Hyperspectral Multispectral Data fusion nonlinear unmixing non negative matrix factorization

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Source Separation Based on non-negative matrix factorization of the Synchrosqueezing Transform 29

Source Separation Based on Non-Negative Matrix Factorization...

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29th European Signal Processing Conference (EUSIPCO)

作者： Singh, Neha Meignen, Sylvain Oberlin, Thomas Univ Grenoble Alpes Jean Kuntzmann Lab Grenoble France Univ Grenoble Alpes ISAE SUPAERO Toulouse France

ISBN: (纸本)9789082797060

In this paper, we consider the problem of single channel blind source separation, for which a very common and effective solution consists of applying non-negative matrix factorization to the spectrogram of the mixture. We here propose to replace the spectrogram by the modulus of the synchrosqueezing transform (SST), which achieves a sharper time-frequency representation. Then we introduce two methods for reconstructing the sources, one based on the direct reconstruction from the synchrosqueezed representation, and the other on a two-step procedure based on both the short-time Fourier transform (STFT) and SST, the latter technique being introduced to deal with large signals. Our experiments suggest that non-negative matrix factorization applied to SST enables a better source separation than when applied to the modulus of STFT, and that the proposed two-step procedure using SST and STFT also performs better than the classical technique based on STFT only.

关键词： Synchrosqueezing transform non negative matrix factorization Short-time Fourier transform time-frequency reassignment

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Variational Autoencoder for non-negative matrix factorization with Exogenous Inputs Applied to Financial Data Modelling 11

Variational Autoencoder for Non-Negative Matrix Factorizatio...

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11th International Symposium on Image and Signal Processing and Analysis (ISPA)

作者： Montesdeoca, Luis Squires, Steven Niranjan, Mahesan Univ Southampton Elect & Comp Sci Southampton England

ISBN: (纸本)9781728131405

Multi-variate time series that arise in financial data, for example, are likely to be driven by underlying lower dimensional latent variables. Extracting such latent spaces can be useful in representing the data efficiently and as a means of explaining aspects of the system from which they are generated. Here, we study an extension to the Variational Autoencoder model specifically cast in a probabilistic setting to deal with positive valued data to extract a non-negative matrix factorization model (NMF) in a probabilistic setting (PAE-NMF). To model financial data, where information about some underlying macroeconomic system may be observed, we extend the PAE-NMF model to include exogenous variables (PAE-XNMF). We present the learning algorithm for this model and illustrate its operation on financial data of constituents of the FTSE100 index and a set of relevant macroeconomic variables. We show an example of the latent space detecting a sharp transition around the Brexit event that is not readily apparent on any of the individual time series.

关键词： non negative matrix factorization Variational Autoencoder Dimensionality Reduction Deep Learning Kull-back Leibler divergence Financial Data Analysis

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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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Performance of Symmetric non-negative matrix factorization-based Community Detector with Learning Depth Variations 15

Performance of Symmetric Non-negative Matrix Factorization-b...

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15th IEEE International Conference on Networking, Sensing and Control (ICNSC)

作者： Qu, Qingxiu Luo, Xin Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China

ISBN: (纸本)9781538650530

Community detection is a popular topic in the area of social network analysis. Symmetric non-negative matrix factorization (SNMF) is widely adopted to address this task. This work focuses on connections between the quality of community detection, and the learning strategies for the non-negative and multiplicative update rule (NMU), which is a popular learning scheme for implementing SNMF model. To do so, we adopt alpha and beta-controlled NMU to implement a series of SNMF-based community detectors, and validate their performance on six different social networks generated by industrial applications. Experimental results demonstrate that a) the learning depth of NMU is closely connected with the resultant model's performance in community detection;b) with large alpha in alpha-controlled NMU, the resultant SNMF model can achieve the most accurate results in community detection;d) compared with the traditional NMU, both alpha and beta-controlled NMU schemes are able to make an SNMF model achieve significantly higher performance in community detection..

关键词： Complex Network Community Detection non negative matrix factorization NMF

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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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Combining non-negative matrix factorization and deep neural networks for speech enhancement and automatic speech recognition 41

Combining non-negative matrix factorization and deep neural ...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016

作者： Vu, Thanh T. Bigot, Benjamin Chng, Eng Siong School of Computer Engineering Nanyang Technological University Singapore Rolls-Royce at NTU Corporate Lab Nanyang Technological University Singapore

ISBN: (纸本)9781479999880

Sparse non-negative matrix factorization (SNMF) and Deep Neural Networks (DNN) have emerged individually as two efficient machine learning techniques for single-channel speech enhancement. Nevertheless, there are only few works investigating the combination of SNMF and DNN for speech enhancement and robust Automatic Speech Recognition (ASR). In this paper, we present a novel combination of speech enhancement components based-on SNMF and DNN into a full-stack system. We refine the cost function of the DNN to back-propagate the reconstruction error of the enhanced speech. Our proposal is compared with several state-of-the-art speech enhancement systems. Evaluations are conducted on the data of CHiME-3 challenge which consists of real noisy speech recordings captured under challenging noisy conditions. Our system yields significant improvements for both objective quality speech enhancement measurements with relative gain of 30%, and a 10% relative Word Error Rate reduction for ASR compared to the best baselines. © 2016 IEEE.

关键词： Automatic Speech Recognition CHiME-3 challenge Deep Neural Network non negative matrix factorization Speech Enhancement

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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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