نتایج جستجو برای: non-negative matrix factorization
تعداد نتایج: 2092099 فیلتر نتایج به سال:
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...
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...
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 ...
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...
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...
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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