Results 231 to 240 of about 27,345 (261)

Tensor Dictionary Learning

2021
Dictionary learning is one of classical data-driven ways for linear feature extraction, which finds wide applications in image recovery and classification, audio processing, biomedical signal processing, and data fusion. As its natural extension for multidimensional data, tensor dictionary learning can extract the multilinear features. The optimization
Yipeng Liu, Jiani Liu, Zhen Long, Ce Zhu
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Kernel Dictionary Learning

2018
Sparse representations are linear by construction, a fact that can hinder their use in classification problems. Building vectors of characteristics from the signals to be classified can overcome the difficulties and is automated by employing kernels, which are functions that quantify the similarities between two vectors.
Bogdan Dumitrescu, Paul Irofti
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Robust Dictionary Learning

2017
The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semi-supervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively.
Sheng Li, Yun Fu
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Dictionary Learning Problem

2018
Dictionary learning can be formulated as an optimization problem in several ways. We present here the basic form, where the representation error is minimized under the constraint of sparsity, and discuss several views and relations with other data analysis and signal processing problems. We study some properties of the DL problem and their implications
Bogdan Dumitrescu, Paul Irofti
openaire   +1 more source

Leveraging seed dictionaries to improve dictionary learning

2016 IEEE International Conference on Image Processing (ICIP), 2016
Most state-of-the-art dictionary learning algorithms (DLAs) are iterative, and must begin with an initial estimate of the dictionary, referred to as the seed. A seed can be generated randomly, but it has been shown that choosing a more intelligent seed often yields a better solution.
Daniel Reichman, Jordan M. Malof, Leslie M. Collins   +2 more
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Dictionary Reduction: Automatic Compact Dictionary Learning for Classification

2017
A complete and discriminative dictionary can achieve superior performance. However, it also consumes extra processing time and memory, especially for large datasets. Most existing compact dictionary learning methods need to set the dictionary size manually, therefore an appropriate dictionary size is usually obtained in an exhaustive search manner. How
Yang Song, Zhifei Zhang, Liu Liu, Alireza Rahimpour, Hairong Qi   +4 more
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When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition With Limited Data

IEEE Transactions on Neural Networks and Learning Systems, 2021
Hao Tang, Hong Liu, Niculae Sebe
exaly  

Dictionary Learning

2022
openaire   +1 more source

Structured discriminant analysis dictionary learning for pattern classification

Knowledge-Based Systems, 2021
Haishun Du
exaly  

Dictionary Learning

2013
Vishal M. Patel, Rama Chellappa
openaire   +1 more source