Results 1 to 10 of about 1,914 (154)
HePPCAT: Probabilistic PCA for Data With Heteroscedastic Noise [PDF]
IEEE Transactions on Signal Processing, 2021 Principal component analysis (PCA) is a classical and ubiquitous method for reducing data dimensionality, but it is suboptimal for heterogeneous data that are increasingly common in modern applications. PCA treats all samples uniformly so degrades when the noise is heteroscedastic across samples, as occurs, e.g., when samples come from sources of ...
Jeffrey A Fessler +2 more
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Optimal Spectral Shrinkage and PCA With Heteroscedastic Noise [PDF]
IEEE Transactions on Information Theory, 2021 This paper studies the related problems of prediction, covariance estimation, and principal component analysis for the spiked covariance model with heteroscedastic noise. We consider an estimator of the principal components based on whitening the noise, and we derive optimal singular value and eigenvalue shrinkers for use with these estimated principal
William Leeb, Elad Romanov
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Heteroscedastic Bias-Robust Projected Gradient Descent for UWB Localization in Complex Indoor Environments [PDF]
SensorsUltra-wideband (UWB) localization is widely used in indoor positioning because it provides high temporal resolution and direct geometric range constraints. In complex indoor environments, however, UWB ranging is affected by non-line-of-sight propagation,
Zhongyang Yu, Qinghua Liu, Yong Qian
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Underwater Noise Modeling and Direction-Finding Based on Heteroscedastic Time Series [PDF]
EURASIP Journal on Advances in Signal Processing, 2007 We propose a new method for practical non-Gaussian and nonstationary underwater noise modeling. This model is very useful for passive sonar in shallow waters.
Kamarei Mahmoud +2 more
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Identifying patient-specific root causes with the heteroscedastic noise model
Journal of Computational Science, 2023 Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category. A few underlying root causes may nevertheless initiate the development of disease within each patient. We therefore focus on identifying patient-specific root causes of disease, which we equate to the sample-specific ...
Thomas A Lasko
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Camera Model Identification Based on the Heteroscedastic Noise Model
IEEE Transactions on Image Processing, 2014 The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the heteroscedastic noise model, which more accurately describes a natural raw image. This model is characterized by only two parameters, which are considered as unique fingerprint to identify camera models.
Thanh Hai Thai +2 more
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Lecture Notes in Computer Science, 2023 In this research, we develop a Bayesian optimization algorithm to solve expensive, constrained problems. We consider the presence of heteroscedastic noise in the evaluations and thus propose a new acquisition function to account for this noise in the search for the optimal point.
Inneke Van Nieuwenhuyse
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ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Mixtures of probabilistic principal component analysis (MPPCA) is a well-known mixture model extension of principal component analysis (PCA). Similar to PCA, MPPCA assumes the data samples in each mixture contain homoscedastic noise. However, datasets with heterogeneous noise across samples are becoming increasingly common, as larger datasets are ...
Jeffrey A Fessler, Laura Balzano
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An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels
Algorithms, 2023 Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heteroscedastic characteristic label noise make chest X-ray image classification more challenging. To address
Qingji Guan, Qinrun Chen, Yaping Huang
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Smooth Twin Parametric Insensitive Support Vector Regression [PDF]
Zhengzhou Daxue xuebao. Gongxue ban, 2022 As one of the machine learning methods, twin parametric insensitive support vector regression (TPISVR) had a simple mathematical model and good learning performance.
HUANG Huajuan, WEI Xiuxi, ZHOU Yongquan
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