NMFk: Nonnegative Matrix Factorization - GitHub
网页NMFk: Nonnegative Matrix Factorization + k-means clustering and physics constraints. NMFk is a module of the SmartTensors ML framework (smarttensors.com). NMFk is a novel unsupervised machine learning methodology that allows for the automatic identification of the optimal number of features (signals/signatures) present in the data.
GitHub - lanl/pyDNMFk: Python Distributed Non Negative Matrix ...
网页pyDNMFk is a software package for applying non-negative matrix factorization in a distributed fashion to large datasets. It can minimize the difference between reconstructed data and the original data through various norms (Frobenius, KL-divergence).
Readme · NMFk.jl
网页NMFk is a novel unsupervised machine learning methodology which allows for automatic identification of the optimal number of features (signals/signatures) present in the data. Classical NMF approaches do not allow for automatic estimation of the number of features. NMFk estimates the number of features k through k-means clustering coupled with regularization constraints (physical, mathematical ...
网页In this paper, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model.
TELF.factorization.NMFk: Non-negative Matrix Factorization with ...
网页NMFk is a Non-negative Matrix Factorization module with the capability to do automatic model determination. Parameters: n_perturbs ( int, optional) – Number of bootstrap operations, or random matrices generated around the original matrix. The default is 20. n_iters ( int, optional) – Number of NMF iterations. The default is 100.
网页Non-negative Matrix Factorization (NMF) has proven to be a powerful unsupervised learning method for uncovering hidden features in complex and noisy data sets with applications in data mining, text recognition, dimension reduction, face recognition, anomaly detection, blind source separation, and many other elds.
NMF降维算法与聚类模型的综合运用 - 知乎专栏
网页NMF本质上说是一种矩阵分解的方法，它的特点是可以将一个大的非负矩阵分解为两个小的非负矩阵，又因为分解后的矩阵也是非负的， 所以也可以继续分解。 因此，当我们数据集大小过于庞大时， NMF算法可以运用解决数据庞大而带来的复杂超额的计算量。 1：简单概括. 我们设数据 D=m∗n ， 其中 m 是数据的个数， n 是数据特征。 当矩阵 D 非负时，我们令 D_ { (m∗n)}=W_ { (m∗k)}∗H_ { (k∗n) } （由代数知识知此解不会唯一，但每个解都是相应变换 …
[2006.12402] A Neural Network for Determination of Latent ...
网页2020年6月22日 · Non-negative Matrix Factorization (NMF) has proven to be a powerful unsupervised learning method for uncovering hidden features in complex and noisy data sets with applications in data mining, text recognition, dimension reduction, face recognition, anomaly detection, blind source separation, and many other fields.
Distributed out-of-memory NMF on CPU/GPU architectures
网页2023年9月8日 · NMF is a popular unsupervised learning method that extracts sparse and explainable latent features [1], which are often used to reveal explainable low-dimensional hidden structures that represent and classify the elements of the whole dataset [2].
Title: Distributed Out-of-Memory NMF on CPU/GPU Architectures
网页2022年2月19日 · We propose an efficient distributed out-of-memory implementation of the Non-negative Matrix Factorization (NMF) algorithm for heterogeneous high-performance-computing (HPC) systems. The proposed implementation is based on prior work on NMFk, which can perform automatic model selection and extract latent variables and …