In recent decades, nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) have been widely exploited for hyperspectral unmixing. To improve the unmixing performance, various constraints have been applied in many NMF-based and NTF-based methods. Though many regularizations are...
Then, under the framework of non-negative tensor factorization (NTF), we propose a novel unmixing algorithm named spatial feature extraction NTF (SFE-NTF) for hyperspectral unmixing. The proposed SFE-NTF is based on an augmented multiplicative algorithm. Experimental results on both synthetic and ...
Non-negative tensor factorization output feature production is closely integrated with a visual post-processing tool, FutureLens, that allows the user to perform in-depth analysis of textual data, facilitating scenario extraction and knowledge discovery....
TobepublishedinOpticsLetters:Title: Blind Multi-spectral Image Decomposition by 3D Nonnegative TensorFactorization Authors: Ivica ..
Nonnegative Tensor FactorizationNonnegative Matrix FactorizationSeparable factorization modelXRAY algorithmBlind Source SeparationMany computational problems in machine learning can be represented by separable matrix factorization models. In a geometric approach, linear separability means that the whole set of ...
Advances in Nonnegative Matrix and Tensor Factorization A. Cichocki,M. Mørup,P. Smaragdis,W. Wang,R. Zdunek Computational Intelligence and Neuroscience First Published:06 July 2008 Full text PDF Research Article Open Access Pattern Expression Nonnegative Matrix Factorization: Algorithm and Applications...
referred to as nonnegative Tucker decomposition (NTD). The main contributions of this paper include: (1) multi- plicative updating algorithms for NTD; (2) an initialization method for speeding up convergence; (3) a sparseness con- trol method in tensor factorization. Through several com- puter...
A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis. It addresses signal variability caused by stretching, proving beneficial for analyzing data such as powder diffraction at varying temperatures...
Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extrem
2) nonnegative decomposition 非负分解3) Tensor decomposition 张量分解 1. The tensor decomposition is used to extract the distribution feature of the sliced data. 本文对基于分布式的演化数据流的连续异常检测问题进行了形式化描述,提出一种在滑动窗口中基于张量分解的异常检测算法——WSTA。 更多例句>> ...