Kernel Method based on Non-Linear Coherent State

Tiwari, Prayag; Dehdashti, Shahram; Obeid, Abdul Karim; Melucci, Massimo; Bruza, Peter

doi:10.48550/arxiv.2007.07887

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…A set of dataset is employed to demonstrate how the parameter 0  affects the FAR. The dataset is taken from sklearn [35], to serve as an example of linear non-separable data with Gaussian noise. A detailed introduction is presented in Section IV.…”

Section: ) Penalty Parameters For Far Controllabilitymentioning

confidence: 99%

A False-Alarm-Controllable Modified AdaBoost Wake Detection Method Using SAR Images

Guan,

Xu,

Liu

et al. 2023

IEEE Sensors J.

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A false-alarm-controllable modified AdaBoost based method is proposed for detecting ship wake from sea clutter in synthetic aperture radar (SAR) images. It reformulates the wake detection problem as a binary classification task in the multi-feature space. The update strategy of the sample weights in the original AdaBoost is modified for wake detection. First, a detection result confidence factor is designed to deal with class imbalance between sea clutter and ship wake; then, the AdaBoost is further modified as a false alarm rate (FAR) controllable detector by introducing penalty parameters to adjust weights update strategies for the sea clutter. Meanwhile, the multi-feature space is spanned by a novel frequency peak height ratio (FPHA) feature and four salient features. FPHA is proposed to enhance separation between the wake and sea clutter, which is computed from the amplitude spectrum peak of the image after Fourier transform. Experimental results show that the proposed detector can tackle the imbalanced data problem and flexibly control FAR by adjusting penalty parameters. Moreover, improved detection probability is also achieved compared with existing methods.

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Section: ) Penalty Parameters For Far Controllabilitymentioning

confidence: 99%

A False-Alarm-Controllable Modified AdaBoost Wake Detection Method Using SAR Images

Guan,

Xu,

Liu

et al. 2023

IEEE Sensors J.

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“…In particular, the methods in this paper are in the subcategory of QML called quantum inspired classical machine learning, where theory and methods from quantum physics are borrowed and adapted to machine learning methods intended to run in classical computers. Works in this category include: quantum-inspired recommendation systems (Tang, 2019a), quantum-inspired kernel-based classification methods (Tiwari et al, 2020;González et al, 2020), conversational sentiment analysis based on density matrix-like convolutional neural networks (Zhang et al, 2019), dequantised principal component analysis (Tang, 2019b), among others.…”

Section: Related Workmentioning

confidence: 99%

Learning with Density Matrices and Random Features

González,

Gallego,

Toledo-Cortés

et al. 2021

Preprint

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A density matrix describes the statistical state of a quantum system. It is a powerful formalism to represent both the quantum and classical uncertainty of quantum systems and to express different statistical operations such as measurement, system combination and expectations as linear algebra operations. This paper explores how density matrices can be used as a building block for machine learning models exploiting their ability to straightforwardly combine linear algebra and probability. One of the main results of the paper is to show that density matrices coupled with random Fourier features could approximate arbitrary probability distributions over R n . Based on this finding the paper builds different models for density estimation, classification and regression. These models are differentiable, so it is possible to integrate them with other differentiable components, such as deep learning architectures and to learn their parameters using gradient-based optimization. In addition, the paper presents optimizationless training strategies based on estimation and model averaging. The models are evaluated in benchmark tasks and the results are reported and discussed.

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qCLUE: a quantum clustering algorithm for multi-dimensional datasets

Gopalakrishnan,

Dellantonio,

Di Pilato

et al. 2024

Front. Quantum Sci. Technol.

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Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges stemming from datasets that span more spatial dimensions. In fact, the best-performing clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qCLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qCLUE is inspired by CLUE, an algorithm developed to address the challenging time and memory budgets of Event Reconstruction (ER) in future High-Energy Physics experiments. As such, qCLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qCLUE in several scenarios, demonstrating its effectiveness and proving it to be a promising route to handle complex data analysis tasks – especially in high-dimensional datasets with high densities of points.

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Kernel Method based on Non-Linear Coherent State

Cited by 3 publications

References 35 publications

A False-Alarm-Controllable Modified AdaBoost Wake Detection Method Using SAR Images

A False-Alarm-Controllable Modified AdaBoost Wake Detection Method Using SAR Images

Learning with Density Matrices and Random Features

qCLUE: a quantum clustering algorithm for multi-dimensional datasets

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