Sparse dictionary learning

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Algorithms

Sparse coding Orthogonal Matching Pursuit LASSO Frobenius norm Moore-Penrose pseudoinverse

16 related topics

Overview

Representation learning Sparse Linear combination Orthogonal Sparsity

17 related topics

Problem statement

Optimization problem L0-"norm"-norm Convex optimization Dimensionality reduction

7 related topics

Applications

Bag-of-Words

1 related topics

Topics to explore

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Overview

Representation learning
Sparse Sparse matrix
Linear combination
Orthogonal Orthogonal basis
Sparsity
Compressed sensing
Signal recovery Detection theory
Wavelet Wavelet transform
Basis pursuit
Signal processing
Fourier Fourier transform
Compression Data compression
Analysis Data analysis
Denoising Noise reduction
Classification Image classification
Audio processing Audio signal processing
Inpainting

Problem statement

Algorithms

Sparse coding Sparse approximation
Orthogonal Matching Pursuit Matching pursuit
LASSO Lasso (statistics)
Frobenius norm
Moore-Penrose pseudoinverse Moore–Penrose pseudoinverse
K-SVD
SVD Singular value decomposition
K-means K-means clustering
Rank-1 approximation Low-rank approximation
Dual Lagrangian problem Duality (optimization)
Newton's method Newton's method in optimization
Conjugate gradient Conjugate gradient method
Stream Stream (computing)
Online learning Online machine learning
LARS Least-angle regression
Block-coordinate Coordinate descent

Applications

Bag-of-Words Bag-of-words model in computer vision

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Map overview Semantic statistics

Nodes46

Edges45

Triples24

Avg. degree1.96

Density0.043478

Components1

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Sparse dictionary learning

Top relations

has application · 6

Sparse dictionary learning → Bag-of-Words, In, It, Sparse, The, This

related to Properties of the dictionary · 5

Sparse dictionary learning → And, However, The, This, Undercomplete

related to Online dictionary learning (LASSO approach) · 4

Sparse dictionary learning → However, Many, Such, The

related to Method of optimal directions (MOD) · 2

Sparse dictionary learning → MOD, The

Important terminology Word statistics

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Important terminology

dictionary sparse displaystyle learning signal mathbf data input problem dictionaries representation one atoms sparsity method also coding lambda signals matrix

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
the wavelet transform or the directional gradient of a rasterized matrix	instance of	it is crucial to find a sparse representation of that signal	0.80	text
Fourier or wavelet transforms	instance of	the general practice was to use predefined dictionaries	0.80	text
data analysis or classification	instance of	And dimensionality reduction based on dictionary representation can be extended to address specific tasks	0.80	text
matching pursuit	instance of	MOD alternates between getting the sparse coding using a method	0.80	text
updating the dictionary by computing the analytical solution of the problem given by D	instance of	MOD alternates between getting the sparse coding using a method	0.80	text
fast computation	instance of	is some pre-defined analytical dictionary with desirable properties	0.80	text
A	instance of	is some pre-defined analytical dictionary with desirable properties	0.80	text
Sparse dictionary learning	has application	The	0.60	section
Sparse dictionary learning	has application	This	0.60	section
Sparse dictionary learning	has application	It	0.60	section
Sparse dictionary learning	has application	Sparse	0.60	section
Sparse dictionary learning	has application	In	0.60	section

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Sparse dictionary learning

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Sparse dictionary learning

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