Results 251 to 260 of about 17,677 (287)

An NMF Perspective on Binary Hashing

2015 IEEE International Conference on Computer Vision (ICCV), 2015
The pervasiveness of massive data repositories has led to much interest in efficient methods for indexing, search, and retrieval. For image data, a rapidly developing body of work for these applications shows impressive performance with methods that broadly fall under the umbrella term of Binary Hashing.
Lopamudra Mukherjee, Sathya N. Ravi, Vamsi K. Ithapu, Tyler Holmes, Vikas Singh   +4 more
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From Binary NMF to Variational Bayes NMF: A Probabilistic Approach

2015
A survey of our recent work on probabilistic NMF is provided. All variants discussed here are illustrated by their application to the analysis of failure patterns emerging from manufacturing and processing silicon wafers. It starts with binNMF, a variant developed to apply NMF to binary data sets. The latter are modeled as a probabilistic superposition
R. Schachtner, G. Pöppel, A. M. Tomé, E. W. Lang   +3 more
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NMF with local constraint and Deep NMF with temporal dependencies constraint for action recognition

Neural Computing and Applications, 2018
In order to improve action recognition accuracy, a new nonnegative matrix factorization with local constraint (LC-NMF) is firstly presented. By applying it for effective trajectory clustering, complex backgrounds are removed and then the motion salient regions are obtained.
Ming Tong, Yiran Chen 0002, Lei Ma, He Bai 0010, Xing Yue   +4 more
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Component-Adaptive Priors for NMF

2015
Additional priors for nonnegative matrix factorization NMF are a powerful way of adapting NMF to specific tasks, such as for example audio source separation. For this application, priors supporting sparseness or temporal continuity have been proposed. However, these priors are not helpful for all kinds of signals and should therefore only be used when ...
Julian Mathias Becker, Christian Rohlfing   +1 more
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Font Classification Using NMF

2003
In this paper, we propose a font classification method in scanned documents using non-negative matrix factorization (NMF). Using NMF, we automatically extract spatially local features enough to classify each font. The appropriateness of the features to classify a specific font is shown in the experimental results.
Chang-Woo Lee, Hyun Kang, Keechul Jung, Hang Joon Kim   +3 more
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NMF Based System for Speaker Identification

2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), 2021
Automatic identification of speakers from text-independent information is a task required in a broad base of applications. Gaussian Mixture Models are a state-of-the-art solution to the task. We apply this method to a text-independent speech dataset, and present a novel method using Nonnegative Matrix Factorization and sparseness constraints.
Giovanni Costantini, Valerio Cesarini, Fabio Paolizzo   +2 more
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Hierarchical Representation Using NMF

2013
In this paper, we propose a representation model that demonstrates hierarchical feature learning using nsNMF. We stack simple unit algorithm into several layers to take step-by-step approach in learning. By utilizing NMF as unit algorithm, our proposed network provides intuitive understanding of the feature development process.
Hyun Ah Song, Soo-Young Lee
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NMF with LogGabor Wavelets for Visualization

2005
Many problems in image representation and classification involve some form of dimensionality reduction. Non-negative matrix factorization (NMF) is a recently proposed unsupervised procedure for learning spatially localized, parts-based subspace representation of objects.
Zhonglong Zheng, Jianmin Zhao, Jie Yang 0002   +2 more
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Biological network clustering by robust NMF

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 2014
We propose a Robust Non-negative Matrix Factorization (RNMF) formulation by introducing L1-norm regularization terms for decomposed factors to cluster noisy biological networks for identification of functional modules. To solve robust NMF, we develop an accelerated alternative proximal method, which takes advantages of a fast iterative shrinkage ...
Yijie Wang 0003, Xiaoning Qian
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A problem with (and fix for) variational Bayesian NMF

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014
Probabilistic nonnegative matrix factorization (NMF) models have had great success in audio source separation problems. Bayesian formulations of these models are fit either using Markov chain Monte Carlo or variational inference, with the latter often being preferred for its computational efficiency.
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