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ENDMEMBER EXTRACTION OF HIGHLY MIXED DATA USING L1 SPARSITY-CONSTRAINED MULTILAYER NONNEGATIVE MATRIX FACTORIZATION
Hyperspectral Imagery Nonnegative Matrix Factorization Multilayer Nonnegative Matrix Factorization Sparsity Constraint Endmember Extraction
2018/5/11
Due to the limited spatial resolution of remote hyperspectral sensors, pixels are usually highly mixed in the hyperspectral images. Endmember extraction refers to the process identifying the pure endm...
Efficient Privacy-Preserving Matrix Factorization via Fully Homomorphic Encryption
homomorphic encryption matrix factorization gradient descent
2016/3/4
Recommendation systems become popular in our daily life. It is well known that the more
the release of users’ personal data, the better the quality of recommendation. However, such
services raise se...
When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
Non-Negative Matrix Factorization Parts
2015/8/21
We interpret non-negative matrix factorization geometrically, as the
problem of finding a simplicial cone which contains a cloud of data
points and which is contained in the positive orthant.
Grouping tasks and data display items via the non-negative matrix factorization
Grouping tasks non-negative matrix factorization
2015/8/21
Analyzing work functions and the IO variables they
need is an important component of designing and evaluating
complex systems. We develop a biclustering method for jointly
grouping work functions a...
Estimation and Solution of Linear Rational Expectations Models Using a Polynomial Matrix Factorization
Linear Rational Expectations Models Polynomial Matrix Factorization
2015/8/4
Estimation and Solution of Linear Rational Expectations Models Using a Polynomial Matrix Factorization.
Extracting Deep Neural Network Bottleneck Features using Low-Rank Matrix Factorization
DNN Bottleneck features
2014/11/27
In this paper, we investigate the use of deep neural networks (DNNs) to generate a stacked bottleneck (SBN) feature representation for low-resource speech recognition. We examine different SBN extract...
Extracting Deep Neural Network Bottleneck Features Using Low-Rank Matrix Factorization
DNN Bottleneck features
2015/3/9
Extracting Deep Neural Network Bottleneck Features Using Low-Rank Matrix Factorization.
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization
Structural Functional Discovery Dynamic Networks Non-negative Matrix Factorization
2013/6/17
Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction. This paper describes the development and application of matrix ...
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Data Representation Nonnegtive Matrix Factorization Graph Regularization Multiple Kernel Learning.
2012/9/18
Nonnegative Matrix Factorization (NMF) has been contin-uously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank ...
Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization
ositive matrix factorization Enrichment factor analysis Conditional probability function analysis
2016/3/25
Ambient Particulate Matters (PM10, PM2.5 and PM0.1) were investigated at Shinjung station in New Taipei City, Taiwan.
Samples were collected simultaneously using a dichotomous sampler (Andersen Model...
Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization
nonnegative matrix factorization algorithms separability robustness hyperspectral unmixing linear mixing model pure-pixel assumption
2012/9/17
In this paper, we study the nonnegative matrix factorization problem under the separability assumption (that is, there exists a cone spanned by a small subsetof the columns of the input nonnegative da...
MahNMF: Manhattan Non-negative Matrix Factorization
Non-negative Matrix Factorization (NMF) Nesterov’s Smoothing Method Sparse and Low-rank Matrix Decomposition
2012/9/19
Non-negative matrix factorization (NMF) approximates a non-negative matrix Xby a product of two non-negative low-rank factor matricesWandH. NMF and its extensions minimize either the Kullback-Leibler ...
Accelerated Multiplicative Updates and Hierarchical ALS Algorithms for Nonnegative Matrix Factorization
nonnegative matrix factorization algorithms multiplicative updates hierarchical alternating least squares
2011/9/21
Abstract: Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of applications such as text mining, image processing, hyperspectral data analysis, computational ...
This work introduces SubMF, a parallel divide-and-conquer framework for noisy matrix factorization.
Online algorithms for Nonnegative Matrix Factorization with the Itakura-Saito divergence
Online algorithms Nonnegative Matrix Factorization Itakura-Saito divergence
2011/7/6
Nonnegative matrix factorization (NMF) is now a common tool for audio source separation.