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Everything about Sparse dictionary learning
Representation learning method
Sparse dictionary learning is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary. Atoms in the dictionary are not required to be orthogonal, and they may be an over-complete spanning set. This problem setup also allows the dimensionality of the signals being represented to be higher than any one of the signals being observed. These two properties lead to having seemingly redundant atoms that allow multiple representations of the same signal, but also provide an improvement in sparsity and flexibility of the representation. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Sparse dictionary learningA 5-minute orientation
- Skim a recent paper: Hydrogenerator early fault detection: Sparse Dictionary Learning jointly with the Variational AutoencoderInfluential work since 2022
- Pick up a classic book: Information Processing in Medical ImagingThe most-reprinted book on the subject
- Read the landmark paper: Dictionary Learning Algorithms for Sparse RepresentationCited 662 times
- Go sideways: K-SVDA closely related topic
- Go sideways: Sparse approximationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Dictionary Learning Algorithms for Sparse Representation
Dictionary Learning for Sparse Approximations With the Majorization Method
Dictionary Learning for Sparse Coding: Algorithms and Convergence Analysis
Deformable segmentation via sparse representation and dictionary learning
Cloud Removal Based on Sparse Representation via Multitemporal Dictionary Learning
Fast Dictionary Learning for Sparse Representations of Speech Signals
Locality-sensitive dictionary learning for sparse representation based classification
Learning Discriminative Dictionary for Group Sparse Representation
🆕 Influential since 2022
Hydrogenerator early fault detection: Sparse Dictionary Learning jointly with the Variational Autoencoder
Sparse dictionary learning recovers pleiotropy from human cell fitness screens
Multidomain Kernel Dictionary Learning Sparse Classification Method for Intelligent Machinery Fault Diagnosis
Dictionary learning for sparse representation of signals with hidden Markov model dependency
Deep convolutional dictionary learning network for sparse view CT reconstruction with a group sparse prior
📚 Books worth reading
Information Processing in Medical ImagingJames C. Gee · 2013
Intelligent Science and Intelligent Data EngineeringJian Yang · 2013
Coding the MatrixPhilip N. Klein · 2013
Learning data mining with PythonRobert Layton · 2015
Computer Vision – ACCV 2012Kyoung Mu Lee · 2013
Advances in Multimedia Information Processing – PCM 2012Weisi Lin · 2012- 📕ISMIR 2006International Conference on Music Information Retrieval (7th 2006 Victoria, British Columbia, Canada) · 2006
Quest for the Historical Jesus Christ and ChristianityMr.Avinash Patra · 2011
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- K-SVD In applied mathematics, k-SVD is a dictionary learning algorithm for creating a dictionary for sparse representations, via a singular value decomposition…
- Sparse approximation Sparse approximation (also known as sparse representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding these…
- Convolutional sparse coding The convolutional sparse coding paradigm is an extension of the global sparse coding model, in which a redundant dictionary is modeled as a concatenation…
- K q-flats In data mining and machine learning, k q-flats algorithm is an iterative method which aims to partition m observations into k clusters where each cluster…
- Matching pursuit Matching pursuit (MP) is a sparse approximation algorithm which finds the "best matching" projections of multidimensional data onto the span of an over-complete…
- Representation learning In machine learning (ML), representation learning or feature learning is a set of techniques that allow a system to automatically discover the representations…
- Autoencoder An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns…
- Compressed sensing Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring…