Dictionary learning with equiprobable matching pursuit

Sandin, Fredrik; Martin-del-Campo, Sergio

doi:10.1109/ijcnn.2017.7965902

Cited by 5 publications

(5 citation statements)

References 37 publications

(50 reference statements)

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“…This shows that although slightly outperforms (which itself is more efficient than , see Figure 2B), proves to be closer to the optimal solution given by . Moreover, we replicated in the result of [23] that while homeostasis was essential in improving unsupervised learning, the coding algorithm (MP vs. OMP) mattered relatively little (see Annex ()). Also, we verified the dependence of this efficiency with respect to different hyperparameters (as we did in Figure 2B).…”

Section: Unsupervised Learning and The Optimal Representation Of Isupporting

confidence: 54%

“…Second, we will propose a simplification of this homeostasis algorithm based on the activation probability of each neuron, thanks to the control of the slope of its corresponding Rectifying Linear Unit (ReLU). We show that it yields similar quantitative results as the full homeostasis algorithm and that it converges more rapidly than classical methods [10,23]. We designed our computational architecture to be able to quantitatively cross-validate for every single hyperparameter.…”

Section: Introduction: Reconciling Competition and Cooperationmentioning

confidence: 92%

“…Recently, such an approach was proposed by the authors of [23]. Based on the same observations, the authors proposed to optimize the coding during learning by modulating the gain of each dictionary element based on the recent activation history.…”

Section: Unsupervised Learning and The Optimal Representation Of Imentioning

confidence: 99%

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An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features

Perrinet

2019

Vision

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The formation of structure in the visual system, that is, of the connections between cells within neural populations, is by and large an unsupervised learning process. In the primary visual cortex of mammals, for example, one can observe during development the formation of cells selective to localized, oriented features, which results in the development of a representation in area V1 of images’ edges. This can be modeled using a sparse Hebbian learning algorithms which alternate a coding step to encode the information with a learning step to find the proper encoder. A major difficulty of such algorithms is the joint problem of finding a good representation while knowing immature encoders, and to learn good encoders with a nonoptimal representation. To solve this problem, this work introduces a new regulation process between learning and coding which is motivated by the homeostasis processes observed in biology. Such an optimal homeostasis rule is implemented by including an adaptation mechanism based on nonlinear functions that balance the antagonistic processes that occur at the coding and learning time scales. It is compatible with a neuromimetic architecture and allows for a more efficient emergence of localized filters sensitive to orientation. In addition, this homeostasis rule is simplified by implementing a simple heuristic on the probability of activation of neurons. Compared to the optimal homeostasis rule, numerical simulations show that this heuristic allows to implement a faster unsupervised learning algorithm while retaining much of its effectiveness. These results demonstrate the potential application of such a strategy in machine learning and this is illustrated by showing the effect of homeostasis in the emergence of edge-like filters for a convolutional neural network.

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Section: Unsupervised Learning and The Optimal Representation Of Isupporting

confidence: 54%

Section: Introduction: Reconciling Competition and Cooperationmentioning

confidence: 92%

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An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features

Perrinet

2019

Vision

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“…However, it needs to be noted that the processing time per signal samples decreases logarithmically for increasing signal length. 51 The sparsity level hyperparameter defines the number of atomic events that are generated for the sparse representation, where a lower sparsity level represents a lower number of atomic events. Therefore, the computational cost under the scenario of 5% sparsity level is half of the scenario of 10% sparsity level.…”

Section: Resultsmentioning

confidence: 99%

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

Martin-del-Campo

Sandin

Schnabel³

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

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“…Matching Pursuit (MP) is a sparse approximation algorithm which finds the "best matching" [13], [19]. A extensions of MP is orthogonal MP (OMP) [15], [20], which is applicable to high-dimensional signals.…”

Section: Introductionmentioning

confidence: 99%

Transient Thermal Modeling of Power Semiconductors for Long-Term Load Profiles

Zhang

Zhou

et al. 2023

2023 IEEE Applied Power Electronics Conference and Exposition (APEC)

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This paper presents an improved thermal analysis method focusing on the long-term profiles of power modules. A shift-invariant dictionary method is developed to extract the critical atoms to sparsely represent time series signals.Then the temperature response of the power module can be approximated by a sparse linear combination of basis functions. The performance of the proposed method is compared with the convolution method, the result indicates that the transient junction temperature of the power module can be predicted with less computational effort. The simulation and experimental results also prove that the proposed method can effectively reconstruct the thermal behavior, exhibits good predictive capability and is suitable for temperature prediction under long time scales.

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Dictionary learning with equiprobable matching pursuit

Cited by 5 publications

References 37 publications

An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features

An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

Transient Thermal Modeling of Power Semiconductors for Long-Term Load Profiles

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