A New Criterion for Bounded Component Analysis

10.36227/techrxiv.16744216 ◽

2021 ◽

Author(s):

Renan Brotto ◽

Kenji Nose-Filho ◽

João M. T. Romano

Keyword(s):

Blind Source Separation ◽

Source Separation ◽

Component Analysis ◽

Rotation Matrix ◽

Separation Problem ◽

Correlated Sources ◽

New Approach ◽

Bounded Component ◽

New Criterion

<div>In this paper we present a new criterion for bounded component analysis, a quite new approach for the Blind Source Separation problem. For the determined case, we show that the `1-norm of the estimated sources can be used as a contrast for the problem. We present a blind algorithm for the source separation of independents sources or mixtures of correlated sources by only a rotation matrix. We also present a variety of simulations assessing the performance of the proposed approach.</div>

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Blind Source Separation Based on Non-Gaussianity

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.532-533.1378 ◽

2012 ◽

Vol 532-533 ◽

pp. 1378-1383

Author(s):

Bai Zhan Yang

Keyword(s):

Independent Component Analysis ◽

Blind Source Separation ◽

Source Separation ◽

Component Analysis ◽

Independent Component ◽

Time Structure ◽

Separation Problem ◽

Gaussian Source ◽

Gaussian Signal ◽

Non Gaussian

Independent component analysis is an efficient way to solve blind source separation, which has been broadly used in many fields, such as speech recognition, image processing, wireless communication system, biomedical signal processing etc. Independent component analysis for the traditional ways to solve the blind source separation problem only considers the non-Gaussian signal, without taking into account the time structure of the signal information. Proposed based on generalized self-related and non-Gaussian source separation method, the full account of the non-Gaussian signal and time structure information, to solve the blind source separation problem in the time structure of the signal. Finally, this simulation method is validated, the simulation results show that the method is effective and worthy of promotion.

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Blind-source-separation algorithm based on bounded-component analysis

2016 5th International Symposium on Next-Generation Electronics (ISNE) ◽

10.1109/isne.2016.7543406 ◽

2016 ◽

Author(s):

Chuen-Yau Chen ◽

Wei-Ching Liu ◽

Bo-Wei Lin ◽

Cheng-Yuan Lin

Keyword(s):

Blind Source Separation ◽

Source Separation ◽

Component Analysis ◽

Separation Algorithm ◽

Bounded Component

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Temporal Unmixing of Dynamic Fluorescent Images by Blind Source Separation Method with a Convex Framework

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/713424 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Duofang Chen ◽

Jimin Liang ◽

Kui Guo

Keyword(s):

Blind Source Separation ◽

Tomographic Reconstruction ◽

Source Separation ◽

Component Analysis ◽

Measured Data ◽

Imaging Data ◽

Correlated Sources ◽

Tomographic Images ◽

The Relationship

By recording a time series of tomographic images, dynamic fluorescence molecular tomography (FMT) allows exploring perfusion, biodistribution, and pharmacokinetics of labeled substances in vivo. Usually, dynamic tomographic images are first reconstructed frame by frame, and then unmixing based on principle component analysis (PCA) or independent component analysis (ICA) is performed to detect and visualize functional structures with different kinetic patterns. PCA and ICA assume sources are statistically uncorrelated or independent and don’t perform well when correlated sources are present. In this paper, we deduce the relationship between the measured imaging data and the kinetic patterns and present a temporal unmixing approach, which is based on nonnegative blind source separation (BSS) method with a convex analysis framework to separate the measured data. The presented method requires no assumption on source independence or zero correlations. Several numerical simulations and phantom experiments are conducted to investigate the performance of the proposed temporal unmixing method. The results indicate that it is feasible to unmix the measured data before the tomographic reconstruction and the BSS based method provides better unmixing quality compared with PCA and ICA.

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