2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)最新文献_第3页

Temporal filtering and oriented PCA neural networks for blind source separation 时域滤波与定向PCA神经网络的盲源分离

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318036

K. Diamantaras, Theophilos Papadimitriou

引用次数: 2

Flexible multichannel blind deconvolution, an investigation 柔性多通道盲反褶积的研究

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318034

A. Tsoi, Liangsuo Ma

{"title":"Flexible multichannel blind deconvolution, an investigation","authors":"A. Tsoi, Liangsuo Ma","doi":"10.1109/NNSP.2003.1318034","DOIUrl":"https://doi.org/10.1109/NNSP.2003.1318034","url":null,"abstract":"In this paper, we consider the issue of devising a flexible nonlinear function for multichannel blind deconvolution. In particular, we consider the underlying assumption of the source probability density functions. We consider two cases, when the source probability density functions are assumed to be uni-modal, and multimodal respectively. In the unimodal case, there are two approaches: Pearson function and generalized exponential function. In the multimodal case, there are three approaches: mixture of Gaussian functions, mixture of Pearson functions, and mixture of generalized exponential functions. It is demonstrated through an illustrating example that the assumption on the source probability density functions gives rise to different performances of source separation algorithms for the multichannel blind deconvolution problem. Further it is observed that these performance differences are not large, indicating that the current formulation of multichannel blind deconvolution problems is robust with respect to the underlying assumption of source probability density functions. It is further speculated that one of the discriminating features among various source separation algorithms appears to be the relative computational efficiencies of various approximation schemes. In other words, the discriminating feature of various source separation algorithms based on assumptions on the source probability density function appears to be an implementation issue rather than one of a theoretical concern.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132867315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Blending of missile control modes with neural networks 导弹控制模式与神经网络的融合

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318012

J. Farges, P. Fabiani, S. L. Ménec

引用次数: 0

Fast and efficient sequential learning algorithms using direct-link RBF networks 基于直连RBF网络的快速高效的顺序学习算法

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318020

V. Asirvadam, S. McLoone, G. Irwin

引用次数: 10

Improving independent component analysis performances by variable selection 通过变量选择提高独立成分分析性能

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318035

F. Vrins, J. Lee, M. Verleysen, V. Vigneron, C. Jutten

引用次数: 16

Underdetermined blind separation of sparse sources with instantaneous and convolutive mixtures 瞬态和卷积混合稀疏源的欠定盲分离

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318027

D. Luengo, I. Santamaría, L. Vielva, C. Pantaleón

引用次数: 14

Recognition of musical instruments by generalized min-max classifiers 基于广义最小-最大分类器的乐器识别

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318055

G. Costantini, A. Rizzi, D. Casali

引用次数: 8

Icasso: software for investigating the reliability of ICA estimates by clustering and visualization Icasso:通过聚类和可视化来调查ICA估计可靠性的软件

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318025

J. Himberg, Aapo Hyvärinen

{"title":"Icasso: software for investigating the reliability of ICA estimates by clustering and visualization","authors":"J. Himberg, Aapo Hyvärinen","doi":"10.1109/NNSP.2003.1318025","DOIUrl":"https://doi.org/10.1109/NNSP.2003.1318025","url":null,"abstract":"A major problem in application of independent component analysis (ICA) is that the reliability of the estimated independent components is not known. Firstly, the finite sample size induces statistical errors in the estimation. Secondly, as real data never exactly follows the ICA model, the contrast function used in the estimation may have many local minima which are all equally good, or the practical algorithm may not always perform properly, for example getting stuck in local minima with strongly suboptimal values of the contrast function. We present an explorative visualization method for investigating the relations between estimates from FastICA. The algorithmic and statistical reliability is investigated by running the algorithm many times with different initial values or with differently bootstrapped data sets, respectively. Resulting estimates are compared by visualizing their clustering according to a suitable similarity measure. Reliable estimates correspond to tight clusters, and unreliable ones to points which do not belong to any such cluster. We have developed a software package called Icasso to implement these operations. We also present results of this method when applying Icasso on biomedical data.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124257052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 255

FPGA implementation of bidirectional associative memory using simultaneous perturbation 同时摄动双向联想存储器的FPGA实现

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318029

Y. Maeda, M. Wakamura

引用次数: 0

Blind signal separation by matching pursuit based grouping 基于分组匹配跟踪的盲信号分离

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318038

Y. Huang, R. Dony

引用次数: 0