Recently in Thailand, the recycling process of waste printed circuit board (WPCB) has retained a large volume of nonmetallic fraction (NMF), which has entered the industrial waste stream and awaits an appropriate treatment to be suggested. The aim of this paper was to assess environmental impacts of the recycled nonmetallic fraction from waste printed circuit board in Thailand, using the ReCiPe...

Nonnegative matrix factorization (NMF) has been successfully applied in di erent elds, such as text mining, image processing, and video analysis. NMF is the problem of determining two nonnegative low rank matrices U and V , for a given input matrix M , such that M ≈ UV >. There is an increasing interest in parallel and distributed NMF algorithms, due to the high cost of centralized NMF on large...

The non-negative matrix factorization (NMF) determines a lower rank approximation of a matrix where an interger "!$# is given and nonnegativity is imposed on all components of the factors % & (' and % )'* ( . The NMF has attracted much attention for over a decade and has been successfully applied to numerous data analysis problems. In applications where the components of the data are necessaril...

In order to solve the problem of algorithm convergence in projective non-negative matrix factorization (P-NMF), a method, called convergent projective non-negative matrix factorization (CP-NMF), is proposed. In CP-NMF, an objective function of Frobenius norm is defined. The Taylor series expansion and the Newton iteration formula of solving root are used. An iterative algorithm for basis matrix...

Data Clustering has been an active area of research in many different application areas, with existing clustering algorithms mostly focusing on partitioning one modality or representation of the data. In this study, we delineate and demonstrate a new, enhanced data clustering approach whose innovation is its exploitation of multiple data modalities. We propose BI-NMF, a bi-modal clustering appr...

We propose a simple and efficient approach to learning sparse models. Our approach consists of (1) projecting the data into a lower dimensional space, (2) learning a dense model in the lower dimensional space, and then (3) recovering the sparse model in the original space via compressive sensing. We apply this approach to Non-negative Matrix Factorization (NMF), tensor decomposition and linear ...

Along with the growth of the Internet, automatic recommender systems have become popular. Due to being intuitive and useful, factorization based models, including the Nonnegative Matrix Factorization (NMF) model, are one of the most common approachs for building recommender systems. In this study, we focus on how a recommender system can be built for online services and how the parameters of an...

This paper presents a solution to the Smart Grid case at the Transformation Tool Contest (TTC) 2017 using the .NET Modeling Framework (NMF). The goal of this case was to create incremental views of multiple models relevant in the area of smart grids. Our solution uses the incremental model transformation language NMF Synchronizations and the underlying incrementalization system NMF Expressions.

Nonnegative Matrix Factorization(NMF) is a common used technique in machine learning to extract features out of data such as text documents and images thanks to its natural clustering properties. In particular, it is popular in image processing since it can decompose several pictures and recognize common parts if they’re located in the same position over the photos. This paper’s aim is to prese...

The mechanism of the antitumor action of N-methylformamide (NMF), an agent currently undergoing clinical trials, and its congener, N,N-dimethylformamide (DMF), was examined in the DLD-1 Clone A human colon carcinoma cell line in vitro. The primary action of NMF and DMF on these cells is a depletion of cellular reduced glutathione levels which results in cytostasis. Evidence to support this hypo...