نتایج جستجو برای: non-negative matrix factorization

تعداد نتایج: 2092099  

Journal: :iranian journal of medical physics 0

introduction non-invasive fluorescent reflectance imaging (fri) is used for accessing physiological and molecular processes in biological media. the aim of this article is to separate the overlapping emission spectra of quantum dots within tissue-equivalent phantom using svd, jacobi svd, and nmf methods in the fri mode. materials and methods in this article, a tissue-like phantom and an optical...

Journal: :IEEE Transactions on Pattern Analysis and Machine Intelligence 2019

Journal: :Research Journal of Applied Sciences, Engineering and Technology 2013

Journal: :The Computer Journal 2021

Abstract Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to mining machine learning community, due its advantages such as simple form, good interpretability less storage space. In this paper, we give detailed survey on existing NMF methods, including comprehensive analysis of their design principles, characteristics d...

ژورنال: :فصل نامه علمی پژوهشی مهندسی پزشکی زیستی 2011
امیر حسین اسکندری احسان صداقت نژاد سید جواد موسوی محسن اصغری محمد پرنیان پور

انتخاب الگوی فعال شدن عضلات برای رسیدن به یک هدف خاص به علت پیچیدگی های سیستم اسکلتی عضلانی و نحوه غلبه سیستم اعصاب مرکزی به این پیچیدگی ها، چندین دهه مورد علاقه محققان در این زمینه بوده است. یکی از پاسخ هایی که در این زمینه مطرح شده است، وجود واحدهای (سینرجی) ساده ایست که از ترکیب آن هافعالیت های پیچیده صورت می پذیرند.در این تحقیق وجود و همچنین نحوه آرایش این سینرجی ها در ناحیه کمر مورد بررسی ...

B. Sabzalian V. Abolghasemi

Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...

2003
Sven Behnke

Discovering a representation that reflects the structure of a dataset is a first step for many inference and learning methods. This paper aims at finding a hierarchy of localized speech features that can be interpreted as parts. Non-negative matrix factorization (NMF) has been proposed recently for the discovery of parts-based localized additive representations. Here, I propose a variant of thi...

2009
Mikkel N. Schmidt Ole Winther Lars Kai Hansen

We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates. We discuss how the Gibbs sampler can be used for model ...

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