Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models

Liu, Yang; Heiselman, Cassandra; Quirk, J. Gerald; Djurić, Petar M.

doi:10.1109/icassp39728.2021.9414754

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Furthermore, Yang, Heiselman, Quirk, and Djurić [12] introduced the GPLVM (Gaussian Process Latent Variable Model) as a completely non-linear, probabilistic latent variable structure based on PPCA. This model has the capability to acquire a non-lineal mapping from latent spaces to observation spaces.…”

Section: Global Feature Learningmentioning

confidence: 99%

Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models

Endo

2023

JRS

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Representation learning or feature learning refers to a collection of methods employed in machine learning, which allows systems to autonomously determine representations needed for classifications or feature detection from unprocessed data. Representation learning algorithms are specifically crafted to acquire knowledge of conceptual features that define data. The field of state representation learning is centered on a specific type of representation learning that involves the acquisition of low-dimensional learned features that undergo temporal evolution and are subject to the influence of an agent's actions. Over the past few years, deep architecture have been widely employed for representation learning and have demonstrated exceptional performance in various tasks, including but not limited to object detection, speech recognition, and image classification. This article provides a comprehensive overview of the evolution of techniques for data representation learning. Our research focuses on the examination of conventional feature learning algorithms and advanced deep learning models. This paper presents an introduction to data representation learning history, along with a comprehensive list of available resources such as online courses, tutorials, and books. Additionally, various tool-boxes are also provided for further exploration in this field. In conclusion, this article presents remarks and future prospects for data representation learning.

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Section: Global Feature Learningmentioning

confidence: 99%

Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models

Endo

2023

JRS

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Harnessing the power of ML for robust SISO and MIMO FSO communication systems in fog weather

El-Mottaleb,

Elhefny,

Métwalli

et al. 2024

Opt Quant Electron

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This study examines Free Space Optical (FSO) communication’s performance in different fog conditions, focusing on Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) setups. In low fog, SISO handles signal degradation well. MIMO improves link robustness in moderate fog. High fog challenges traditional FSO, leading to ML integration to optimize communication parameters. For weather channel classification, a preprocessing scheme reduces features from 12 to 2 given that the 12 features are Bit Error Rate (BER), Quality Factor (Q-factor) and Received Optical Power (ROP) for different 4 users. A Gaussian Process Classifier (GPC) achieves an accuracy greater than 99%, surpassing SVM, Decision Tree, and Random Forest. GPC’s fit and predict functions execute in 0.15 s, outperforming NuSVM (0.2 s). This study highlights FSO, SISO, MIMO, and Machine Learning (ML) practicality in enhancing communication resilience in adverse weather, especially in fog-prone areas.

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Identification of Uterine Contractions by An Ensemble of Gaussian Processes

Liu

Heiselman

Quirk

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Identifying uterine contractions with the aid of machine learning methods is necessary vis-á-vis their use in combination with fetal heart rates and other clinical data for the assessment of a fetus wellbeing. In this paper, we study contraction identification by processing noisy signals due to uterine activities. We propose a complete four-step method where we address the imbalanced classification problem with an ensemble Gaussian process classifier, where the Gaussian process latent variable model is used as a decision-maker. The results of both simulation and real data show promising performance compared to existing methods.

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Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models

Cited by 4 publications

References 22 publications

Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models

Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models

Harnessing the power of ML for robust SISO and MIMO FSO communication systems in fog weather

Identification of Uterine Contractions by An Ensemble of Gaussian Processes

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