Results 21 to 30 of about 6,752,036 (307)
Class-Specific Sparse Principal Component Analysis for Visual Classification
IEEE Access, 2020 Extensive research has demonstrated that dictionary learning is active in improving the performance of the representation based classification. However, dictionary learning suffers from lacking an effective dictionary structure that can well tradeoff the
Fei Pan +3 more
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Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification
Remote Sensing, 2021 In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification.
Yachao Zhang +4 more
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Kernel dictionary learning [PDF]
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012 In this paper, we present dictionary learning methods for sparse and redundant signal representations in high dimensional feature space. Using the kernel method, we describe how the well-known dictionary learning approaches such as the method of optimal directions and K-SVD can be made nonlinear.
Hien Van Nguyen +3 more
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Classification-oriented Incremental Dictionary Learning Algorithm [PDF]
Jisuanji gongcheng, 2017 Aiming at the problem that the computation cost of the traditional classification-oriented dictionary learning algorithms is too expensive on big datasets,this paper proposes a novel classification-oriented incremental dictionary learning algorithm.In ...
ZHANG Zhiwu,JING Xiaoyuan,WU Fei
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Online convolutional dictionary learning [PDF]
2017 IEEE International Conference on Image Processing (ICIP), 2017 Accepted to be presented at ICIP ...
Jialin Liu 0003 +3 more
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Ultrasound tomography with learned dictionaries [PDF]
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scanners. Given the measurements, our algorithm finds a sparse image representation in an overcomplete dictionary that is adapted to the properties of UST images.
Ivana Tosic +4 more
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Dictionary Learning with Accumulator Neurons
Proceedings of the International Conference on Neuromorphic Systems 2022, 2022 The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video
Gavin Parpart +8 more
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Online Graph Dictionary Learning
, 2021 International audienceDictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements.
Flamary, Rémi +4 more
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On the Minimax Risk of Dictionary Learning [PDF]
IEEE Transactions on Information Theory, 2016 We consider the problem of learning a dictionary matrix from a number of observed signals, which are assumed to be generated via a linear model with a common underlying dictionary. In particular, we derive lower bounds on the minimum achievable worst case mean squared error (MSE), regardless of computational complexity of the dictionary learning (DL ...
Jung, Alexander +3 more
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Identifiability of Complete Dictionary Learning [PDF]
SIAM Journal on Mathematics of Data Science, 2019 Sparse component analysis (SCA), also known as complete dictionary learning, is the following problem: Given an input matrix $M$ and an integer $r$, find a dictionary $D$ with $r$ columns and a matrix $B$ with $k$-sparse columns (that is, each column of $B$ has at most $k$ non-zero entries) such that $M \approx DB$.
Cohen, Jérémy, Gillis, Nicolas
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