A Self-Stabilizing Learning Rule for Minor Component Analysis

Möller, Ralf

doi:10.1142/s0129065704001863

Cited by 50 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A computationally efficient implementation can be achieved by building on results from the artificial neural networks research community. The latter has devoted much effort to solving eigenvector problems related to autocovariance matrices, which has led to a multitude of efficient algorithms to perform this task [4]. For our purposes, we employ the algorithm proposed by Douglas et al in [5].…”

Section: B Tracking Of N Using a Neural Networkmentioning

confidence: 99%

Modulation-Format-Transparent Polarization Tracking Using a Neural Network

Schmidt

Lankl

2013

IEEE Photon. Technol. Lett.

View full text Add to dashboard Cite

We propose a novel technique for modulationformat-transparent polarization tracking and demultiplexing in a coherent, polarization multiplexed fiber-optic communication system. The technique employs Stokes space analysis of the received signal, which allows us to uniquely identify the polarization state independently of the modulation format. A neural network is used for acquisition and tracking of the polarization state and the obtained information is subsequently used to update an adaptive filter that performs the actual demultiplexing.

show abstract

Section: B Tracking Of N Using a Neural Networkmentioning

confidence: 99%

Modulation-Format-Transparent Polarization Tracking Using a Neural Network

Schmidt

Lankl

2013

IEEE Photon. Technol. Lett.

View full text Add to dashboard Cite

show abstract

“…Recently, a few self-stabilizing MCA algorithms are proposed in [7,28], how is the performance improvement of our TLS neuron compared with the above-mentioned algorithms? Following the analysis method [5,18], the weight norm of above three algorithms can be obtained as follows:…”

Section: The Tls Linear Neuron With a Self-stabilizing Algorithmmentioning

confidence: 99%

“…Above learning curves indicate that our algorithm is good for larger learning factor or under higher noise environments. In order to compare the performance of the proposed algorithm with other congener self-stabilizing algorithms [7,28], another simulation experiment is also performed. The performance comparison between the proposed algorithm and the algorithms [7,28] is shown in Figs.…”

Section: Computer Simulationsmentioning

confidence: 99%

A Self-Stabilizing Neural Algorithm for Total Least Squares Filtering

Kong

Han

2009

Neural Process Lett

View full text Add to dashboard Cite

A neural approach for solving the total least square (TLS) problem is presented in the paper. It is based on a linear neuron with a self-stabilizing neural algorithm, capable of resolving the TLS problem present in the parameter estimation of an adaptive FIR filters for system identification, where noisy errors affect not only the observation vector but also the data matrix. The learning rule is analyzed mathematically and the condition to guarantee the stability of algorithm is educed. The computer simulations are given to illustrate that the neural approach is self-stabilizing and considerably outperforms the existing TLS methods when a larger learning factor is used or the signal-noise-rate is lower.

show abstract

“…Gaussian processes classification (GPC) represents one of the most important practical Bayesian classification methods [30, 31]. Overviews and principles of Gaussian process and its classification applications can be found in [30] and [31].…”

Section: Related Workmentioning

confidence: 99%

Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

Wang

Bishop

Ding

et al. 2007

Sensors

View full text Add to dashboard Cite

The recent availability of low cost and miniaturized hardware has allowed wireless sensor networks (WSNs) to retrieve audio and video data in real world applications, which has fostered the development of wireless multimedia sensor networks (WMSNs). Resource constraints and challenging multimedia data volume make development of efficient algorithms to perform in-network processing of multimedia contents imperative. This paper proposes solving problems in the domain of WMSNs from the perspective of multi-agent systems. The multi-agent framework enables flexible network configuration and efficient collaborative in-network processing. The focus is placed on target classification in WMSNs where audio information is retrieved by microphones. To deal with the uncertainties related to audio information retrieval, the statistical approaches of power spectral density estimates, principal component analysis and Gaussian process classification are employed. A multi-agent negotiation mechanism is specially developed to efficiently utilize limited resources and simultaneously enhance classification accuracy and reliability. The negotiation is composed of two phases, where an auction based approach is first exploited to allocate the classification task among the agents and then individual agent decisions are combined by the committee decision mechanism. Simulation experiments with real world data are conducted and the results show that the proposed statistical approaches and negotiation mechanism not only reduce memory and computation requirements in WMSNs but also significantly enhance classification accuracy and reliability.

show abstract

A Self-Stabilizing Learning Rule for Minor Component Analysis

Cited by 50 publications

References 19 publications

Modulation-Format-Transparent Polarization Tracking Using a Neural Network

Modulation-Format-Transparent Polarization Tracking Using a Neural Network

A Self-Stabilizing Neural Algorithm for Total Least Squares Filtering

Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

Contact Info

Product

Resources

About