Non-negative matrix factorization

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Applications

Dimension reduction Parallel computing Methods of detecting exoplanets Circumstellar disks Linearity

23 related topics

Types

Convex combinations Nonnegative rank Cost functions Regularization Frobenius norm

12 related topics

Algorithms

Multiplicative update rule Matrices of ones Non-negative least squares Gradient descent Active set

9 related topics

Relation to other techniques

Vector quantization Principal component analysis Maximum likelihood Latent class model Directed graphical

8 related topics

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Overview

Algorithms Algorithm
Multivariate analysis
Linear algebra
Matrix Matrix (mathematics)
Factorized Matrix decomposition
Astronomy
Computer vision
Document clustering
Missing data imputation Imputation (statistics)
Chemometrics
Audio signal processing
Recommender systems Recommender system
Bioinformatics

History

Seung Sebastian Seung

Clustering property

Types

Convex combinations Convex combination
Nonnegative rank Nonnegative rank (linear algebra)
Cost functions Loss function
Regularization Regularization (mathematics)
Frobenius norm
Total variation norm
L1 regularization Tikhnov regularization?action=edit&redlink=1
Lasso Lasso (statistics)
Sparse coding
Streaming Data stream
Collaborative filtering
Convolution kernels Kernel (image processing)

Algorithms

Multiplicative update rule Multiplicative Weight Update Method
Matrices of ones Matrix of ones
Non-negative least squares
Gradient descent
Active set
NP-complete
SVD Singular value decomposition
Principal Component Analysis
Karhunen–Loève theorem

Relation to other techniques

Uniqueness

Inverse Inverse matrix
Non-negative Non-negative matrix
Monomial matrix
Permutation

Applications

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Map overview Semantic statistics

Nodes83

Edges82

Triples36

Avg. degree1.98

Density0.024096

Components1

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Non-negative matrix factorization

Top relations

related to Different cost functions and regularizations · 11

Non-negative matrix factorization → Each, Frobenius, Kullback, Lee, Leibler, NMF, Seung, The, There, Two, WH

related to Relation to other techniques · 10

Non-negative matrix factorization → In Learning, It, Kullback, Lee, Leibler, NMF, PCA, Seung, That, When NMF

related to history · 6

Non-negative matrix factorization → Also, Finnish, In, It, Lee, Seung

Important terminology Word statistics

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Important terminology

nmf matrix data non-negative matrices clustering factorization algorithms also displaystyle used one may algorithm using method components two rank analysis

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
processing of audio spectrograms or muscular activity	instance of	in applications	0.80	text
non-negativity is inherent to the data being considered	instance of	in applications	0.80	text
circumstellar disks	instance of	especially for irregularly shaped structures	0.80	text
cell types	instance of	NMF techniques can identify sources of variation	0.80	text
disease subtypes	instance of	NMF techniques can identify sources of variation	0.80	text
population stratification	instance of	NMF techniques can identify sources of variation	0.80	text
tissue composition	instance of	NMF techniques can identify sources of variation	0.80	text
and tumor clonality.A particular variant of NMF	instance of	NMF techniques can identify sources of variation	0.80	text
namely Non-Negative Matrix Tri-Factorization	instance of	NMF techniques can identify sources of variation	0.80	text
Non-negative matrix factorization	related to Different cost functions and regularizations	There	0.60	section
Non-negative matrix factorization	related to Different cost functions and regularizations	The	0.60	section
Non-negative matrix factorization	related to Different cost functions and regularizations	WH	0.60	section

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Applications

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Overview

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Clustering property

Types

Algorithms

Relation to other techniques

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Applications

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Non-negative matrix factorization

How this topic connects Entity context

Non-negative matrix factorization

Top relations

Important terminology Word statistics

Important terminology

Entity relationships Subject–Predicate–Object triples

Related concept clusters Concept neighborhoods

Connections between topic areas Semantic bridges