Dictionary Learning
By Ivana Tosic, Pascal Frossard. IEEE Signal Processing Magazine, 2011
888 citations recorded by the source corpus.
We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification when the learning includes a class separability criteria in the objective function. The benefits of dictionary learning clearly show that a proper understanding of causes underlying the sensed world is key to task-specific representation of relevant information in high-dimensional data sets.
Subfields: Artificial Intelligence, Computational Mechanics, Signal Processing
View Paper: https://www.semanticscholar.org/paper/8824b3d5188491562394f977c15a31599204dfae
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Damek Davis
- Dmitriy Drusvyatskiy
- Ying Wei
- Qiang Yang
- Yu Zheng
- Michael G. Rabbat
- Xiaowen Dong
- Dorina Thanou
In Computational Mechanics
- Damek Davis
- Dmitriy Drusvyatskiy
- Ying Wei
- Xiaowen Dong
- Qiang Yang
- Pascal Frossard
- Michael G. Rabbat
- Sharon Gannot
In Signal Processing
- Yu Zheng
- Qiang Yang
- Emma Ozanich
- Ying Wei
- Pascal Frossard
- Sharon Gannot
- Peter Gerstoft
- Charles-Alban Deledalle
Citations in the corpus
- Machine learning in acoustics: Theory and applications. (2019, methodology): “Newer methods include t-SNE, 27 dictionary learning, 28 and deep representations (e.g., autoencoders).”
- Learning Graphs From Data: A Signal Representation Perspective (2018, background): “Dictionary learning [51], [52] is an area of research in signal processing and machine learning where the signals are represented as a linear combination of simple components, i.”
- Transfer Knowledge between Cities (2016, background): “In [20], the authors summarized three main categories of techniques to learn dictionaries: probabilistic learning, reconstruction error minimization, and clustering.”
- Stochastic model-based minimization of weakly convex functions (2018, background): “The following is a robust variant of the standard relaxation of the problem: More precisely, typical formulations use the squared norm (cid:107) · 22 instead of the norm · (cid:107) 2 ; see e.g. [42, 65].”