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Deepness Analysis of Learned Factors in Multilayer NMF

Ibtissam Brahmi, Guénaël Cabanes, Younès Bennani, Abdelfettah Touzani
2019 Australian Journal of Intelligent Information Processing Systems  
In other words, Multilayer NMF is indeed a hierarchical dimensionality reduction and clustering method.  ...  We conclude that different layers in Multilayer Nonnegative Matrix Factorization are not only dependant but also the order of construction is prominent.  ...  This is to conclude that in fact each learned factor can be expressed in terms of the preceding one, that it depends strongly on the preceding learned factor through all layers of Multilayer NMF, in the  ... 
dblp:journals/ajiips/BrahmiCBT19 fatcat:vgqyno5zyffvvdttdqa3wvq7w4

Bidirectional Nonnegative Deep Model and Its Optimization in Learning

Xianhua Zeng, Zhengyi He, Hong Yu, Shengwei Qu
2016 Journal of Optimization  
Nonnegative matrix factorization (NMF) has been successfully applied in signal processing as a simple two-layer nonnegative neural network.  ...  multilayer deep nonnegative feature representation.  ...  Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant nos. 61672120, 61379114) and the Chongqing Natural Science Foundation Program (Grant no. cstc2015jcyjA40036  ... 
doi:10.1155/2016/5975120 fatcat:bjohpyvkqjdc3arcifhw3dbclm

Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization [article]

Jinshi Yu, Guoxu Zhou, Andrzej Cichocki, Shengli Xie
2018 arXiv   pre-print
Extensive experiments demonstrate the standout performance of the proposed method in clustering analysis.  ...  Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfactory fit to data.  ...  As the success of deep learning, deep learning methods have been widely use in NMF field, such as N-NMF [26] , deep semi-NMF [27] and SDNMF [28] .  ... 
arXiv:1803.07226v1 fatcat:pczy3ilavbe7fmvg6ossfxvtvu

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Xin-Ru Feng, Heng-Chao Li, Rui Wang, Qian Du, Xiuping Jia, Antonio Plaza
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
By extending the decomposition form, involving nonnegative tensor factorization (NTF), multilayer NMF, deep NMF, etc.  ...  Architecture of decomposition for (a) multilayer NMF by factorizing the coefficient matrix, (b) multilayer NMF by factorizing the basis matrix, (c) multilayer convex NMF, and (d) multilayer nsNMF.  ...  processing with the School of Information Science and Technology.  ... 
doi:10.1109/jstars.2022.3175257 fatcat:yqs6eizxbfghhpm4yulzczaxam

Deep Non-Negative Matrix Factorization Architecture based on Underlying Basis Images Learning

Yang Zhao, Huiyang Wang, Jihong Pei
2019 IEEE Transactions on Pattern Analysis and Machine Intelligence  
This image representation method is in line with the idea of "parts constitute a whole" in human thinking. The existing deep NMF performs deep factorization on the coefficient matrix.  ...  Finally, the experimental results show that the deep NMF architecture based on the underlying basis images learning proposed in this paper can obtain better recognition performance than the other state-of-the-art  ...  DEEP NMF ARCHITECTURE BASED ON UNDERLYING BASIS IMAGES LEARNING The existing deep NMF algorithm performs deep factorization on the coefficient matrix.  ... 
doi:10.1109/tpami.2019.2962679 pmid:31899412 fatcat:fu6xpe2pgneudng5h7s63bhb7q

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review [article]

Xin-Ru Feng, Heng-Chao Li, Rui Wang, Qian Du, Xiuping Jia, Antonio Plaza
2022 arXiv   pre-print
Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem.  ...  In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing.  ...  NMF by extending the decomposition form, involving nonnegative tensor factorization (NTF), multilayer NMF, deep NMF, etc.  ... 
arXiv:2205.09933v1 fatcat:77udhvg55fdftidm554qwarqzy

Deep matrix factorizations [article]

Pierre De Handschutter, Nicolas Gillis, Xavier Siebert
2020 arXiv   pre-print
Deep MF was motivated by the success of deep learning, as it is conceptually close to some neural networks paradigms.  ...  Recently, deep matrix factorization (deep MF) was introduced to deal with the extraction of several layers of features and has been shown to reach outstanding performances on unsupervised tasks.  ...  Deep Archetypal Analysis Archetypal analysis (AA) [24] , also known as convex NMF [31] is a variant of NMF in which the basis vectors are constrained to be convex combinations of the data points.  ... 
arXiv:2010.00380v2 fatcat:5d6zleu6w5gh7nmduv2zxu7ep4

Design of Multilayer Perceptrons for Pattern Classifications
패턴인식 문제에 대한 다층퍼셉트론의 설계 방법

Sang-Hoon Oh
2010 The Journal of the Korea Contents Association  
This discussion includes how to decide the number of nodes in each layer, how to initialize the weights of MLPs, how to train MLPs among various error functions, the imbalanced data problems, and deep  ...  In this paper, we discuss the design of MLPs especially for pattern classification problems.  ...  Bengio, "Learning deep architecture for AI," to appear in Foundations and Trends in Machine Learning [32] G. E. Hinton and R.  ... 
doi:10.5392/jkca.2010.10.5.099 fatcat:qqny67qstzhvfaxj5r6fqzesoi

Is Simple Better? Revisiting Non-Linear Matrix Factorization for Learning Incomplete Ratings

Vaibhav Krishna, Tian Guo, Nino Antulov-Fantulin
2018 2018 IEEE International Conference on Data Mining Workshops (ICDMW)  
Firstly, we learn latent factors for representations of users and items from the designed multilayer nonlinear Semi-NMF approach using explicit ratings.  ...  In recent times, different variants of deep learning algorithms have been explored in this setting to improve the task of making a personalized recommendation with user-item interaction data.  ...  ACKNOWLEDGMENT The authors gratefully acknowledge Dijana Tolic for useful directions and comments regarding NMF and Deep Semi-NMF approach.  ... 
doi:10.1109/icdmw.2018.00183 dblp:conf/icdm/KrishnaGA18 fatcat:fpimimjpufa33bpyywkf33moxu

A New Semantic and Statistical Distance-Based Anomaly Detection in Crowd Video Surveillance

Fariba Rezaei, Mehran Yazdi, Alireza Jolfaei
2021 Wireless Communications and Mobile Computing  
Features per frame are computed hierarchically through a pretrained deep model, and in parallel, topic distributions are learned through multilayer nonnegative matrix factorization entangling information  ...  Recently, deep learning (DL) methods have been emerged in various domains, especially CNN for visual problems, with the ability to extract high-level information at higher layers of their architectures  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2021/5513582 fatcat:x5uzynuxrbcc5mlxaew7vbfhsq

Adaptive-Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

Shicheng Li, Qinghua Liu, Jiangyan Dai, Wenle Wang, Xiaolin Gui, Yugen Yi, Wei Quan
2021 Wireless Communications and Mobile Computing  
Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning.  ...  Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together.  ...  [23] applied deep factorization to the basis matrix and proposed a deep NMF method based on basis image learning.  ... 
doi:10.1155/2021/5526479 fatcat:x6iqgyx2hffp7mt7zdpd7maz6m

Deep Self-representative Concept Factorization Network for Representation Learning [article]

Yan Zhang, Zhao Zhang, Zheng Zhang, Mingbo Zhao, Li Zhang, Zhengjun Zha, Meng Wang
2019 arXiv   pre-print
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for  ...  Specifically, DSCF-Net seamlessly integrates the robust deep concept factorization, deep self-expressive representation and adaptive locality preserving feature learning into a unified framework.  ...  Multilayer NMF (MNMF) [11] , Multilayer CF (MCF) [12] , Spectral Unmixing using Multilayer NMF (MLNMF) [13] and Graph Regularized Multilayer CF (GMCF) [14] are some representative models in this  ... 
arXiv:1912.06444v4 fatcat:hasuiek27zhbvaoqhyvxp7wj5u

A Deep Matrix Factorization Method for Learning Attribute Representations

George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Bjorn W. Schuller
2017 IEEE Transactions on Pattern Analysis and Machine Intelligence  
In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes  ...  Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation.  ...  The work of Konstantinos Bousmalis was funded partially from the Google Europe Fellowship in Social Signal Processing.  ... 
doi:10.1109/tpami.2016.2554555 pmid:28113886 fatcat:mimkfywbnbdq5larrpdrfcyhka

Topic Diffusion Discovery Based on Deep Non-negative Autoencoder [article]

Sheng-Tai Huang, Yihuang Kang, Shao-Min Hung, Bowen Kuo, I-Ling Cheng
2020 arXiv   pre-print
Specifically, we propose using a Deep Non-negative Autoencoder with information divergence measurement that monitors evolutionary distance of the topic diffusion to understand how research topics change  ...  The experimental results show that the proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.  ...  there are modified matrix factorization algorithms, such as multi-layer/hierarchical NMF [4] , which incorporate with the multilayer structure.  ... 
arXiv:2010.03710v1 fatcat:7iidqxipobaxtiy5zqmyjjhsly

Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior [article]

Yan Zhang, Zhao Zhang, Yang Wang, Zheng Zhang, Li Zhang, Shuicheng Yan, Meng Wang
2021 arXiv   pre-print
Specifically, DS2CF-Net designs a deep coupled factorization architecture using multi-layers of linear transformations, which coupled updates the bases and new representations in each layer.  ...  In this paper, we technically propose a new enriched prior based Dual-constrained Deep Semi-Supervised Coupled Factorization Network, called DS2CF-Net, for learning the hierarchical coupled representations  ...  Related Deep/Multilayer MF Frameworks We then introduce the architectures of several existing related deep/multilayer matrix factorization algorithms. Traditional multilayer MF.  ... 
arXiv:2009.03714v2 fatcat:fwa2ojfpyjdcrmcm3oidf5v44a
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