scholarly journals Nonnegative Matrix Factorization Numerical Method for Integrated Photonic Cavity Based Spectroscopy

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Zhengyu Huang ◽  
Dayuan Xiong ◽  
Guixu Zhang ◽  
Yiqin Shi ◽  
Changning Hao ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Method ◽  
Spectral Resolution ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Experimental Results ◽  
Theoretical Results

Nonnegative matrix factorization numerical method has been used to improve the spectral resolution of integrated photonic cavity based spectroscopy. Based on the experimental results for integrated photonic cavity device on Optics Letters 32, 632 (2007), the theoretical results show that the spectral resolution can be improved more than 3 times from 5.5 nm to 1.8 nm. It is a promising way to release the difficulty of fabricating high-resolution devices.

scholarly journals Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bing Tang ◽  
Linyao Kang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Collaborative Filtering ◽  
Processing Speed ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Experimental Results ◽  
Computational Time ◽  
Data Sets ◽  
Heterogeneous Cluster ◽  
The Matrix

Nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compression and its capability of extracting highly interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of nonnegative matrix factorization is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU acceleration. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-node Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 CUDA device, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. Furthermore, a GPU-accelerated NMF-based parallel collaborative filtering (CF) algorithm is also proposed, utilizing the advantages of data dimensionality reduction and feature extraction of NMF, as well as the multicore parallel computing mode of CUDA. Using real MovieLens data sets, experimental results have shown that the parallelization of NMF-based collaborative filtering on Spark platform effectively outperforms traditional user-based and item-based CF with a higher processing speed and higher recommendation accuracy.

Measurement and Analysis of the Functional Independence Measure Data by Using Nonnegative Matrix Factorization Method

2013 ◽  
Vol 718-720 ◽  
pp. 630-635 ◽  
Author(s):  
Naoki Yamamoto ◽  
Jun Murakami ◽  
Kei Fujii ◽  
Chiharu Okuma ◽  
Satoko Saito ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Functional Independence Measure ◽  
Factorization Method ◽  
Functional Independence ◽  
Medical Data ◽  
Experimental Results ◽  
Measure Data ◽  
Measurement And Analysis

In this paper, we describe about a manner of adapting the nonnegative matrix factorization (NMF) method to the medical data, especially functional independence measure (FIM) data, and its experimental results. From the results which were obtained by applying the method to actually measured medical data in a hospital, we confirmed that the NMF method was effective to analyze the patients' characteristics related to disability and recovery tendency.

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