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Incremental Methods in Collaborative Filtering for Ordinal Data

4th International Conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Polezhaeva, Elena Moscow MV Lomonosov State Univ Moscow 117234 Russia

ISBN: (纸本)9783642217869

In modern collaborative filtering applications initial data are typically very large (holding millions of users and items) and come in real time. In this case only incremental algorithms are practically efficient. In this paper a new algorithm based on the symbiosis of Incremental Singular Value Decomposition (ISVD) and generalized hebbian algorithm (GHA) is proposed. The algorithm does not require to store the initial data matrix and effectively updates user/item profiles when a new user or a new item appears or a matrix cell is modified. The results of experiments show how root mean square error (RMSE) depends on the number of algorithm's iterations and data amount.

关键词： Collaborative filtering singular value decomposition generalized hebbian algorithm sparse matrix large data ordinal data incremental data

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Linear Replicator in Kernel Space

Linear Replicator in Kernel Space

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7th International Symposium on Neural Networks

作者： Cheng, Wei-Chen Liou, Cheng-Yuan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan

This paper presents a linear replicator [2][4] based on minimizing the reconstruction error [8][9]. It can be used to study the learning behaviors of the kernel principal component analysis [10] the hebbian algorithm ... 详细信息

ISBN: (纸本)9783642133176

关键词： Replicator Principal component analysis generalized hebbian algorithm Kernel hebbian algorithm Gaussian kernel

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Learning principal directions: Integrated-squared-error minimization

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NEUROCOMPUTING 2007年第7-9期70卷 1372-1381页

作者： Ahn, Jong-Hoon Oh, Jong-Hoon Choi, Seungjin Pohang Univ Sci & Technol Dept Comp Sci Pohang 790784 South Korea Pohang Univ Sci & Technol Dept Phys Pohang 790784 South Korea

A common derivation of principal component analysis (PCA) is based on the minimization of the squared-error between centered data and linear model, corresponding to the reconstruction error. In fact, minimizing the squared-error leads to principal subspace analysis where scaled and rotated principal axes of a set of observed data, are estimated. In this paper, we introduce and investigate an alternative error measure, integrated-squared error (ISE), the minimization of which determines the exact principal axes (without rotational ambiguity) of a set of observed data. We show that exact principal directions emerge from the minimization of ISE. We present a simple EM algorithm, 'EM-ePCA', which is similar to EM-PCA [S.T. Roweis, EM algorithms for PCA and SPCA, in: Advances in Neural Information Processing Systems, vol. 10, MIT Press, Cambridge, 1998, pp. 626-632.], but finds exact principal directions without rotational ambiguity. In addition, we revisit the generalized hebbian algorithm (GHA) and show that it emerges from the ISE minimization in a single-layer linear feedforward neural network. (c) 2006 Elsevier B.V. All rights reserved.

关键词： EM algorithm generalized hebbian algorithm generative models probabilistic coupled models separable LS PCA

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A joint compression-discrimination neural transformation applied to target detection

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2005年第4期35卷 670-681页

作者： Chan, AL Der, SZ Nasrabadi, NM USA Res Lab AMSRD ARL SE SE Adelphi MD 20783 USA Aerosp Corp Chantilly VA USA

Many image recognition algorithms based on data-learning perform dimensionality reduction before the actual learning and classification because the high dimensionality of raw imagery would require enormous training sets to achieve satisfactory performance. A potential problem with this approach is that most dimensionality reduction techniques, such as principal component analysis (PCA), seek to maximize the representation of data variation into a small number of PCA components, without considering interclass discriminability. This paper presents a neural-network-based transformation that simultaneously seeks to provide dimensionality reduction and a high degree of discriminability by combining together the learning mechanism of a neural-network-based PCA and a backpropagation learning algorithm. The joint discrimination-compression algorithm is applied to infrared imagery to detect military vehicles.

关键词： automatic target detection eigentargets FLIR imagery generalized hebbian algorithm principal component analysis Sanger's rule

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A new hybrid HMM/ANN model for speech recognition

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2nd International Conference on Artificial Intelligence Applications and Innovations

作者： Xi, XJ Lin, KH Zhou, CL Cai, J Xiamen Univ Software Sch Xiamen Peoples R China

ISBN: (纸本)0387283188

Because of the application of the Hidden Markov Model (HMM) in acoustic modeling, a significant breakthrough has been made in recognizing continuous speech with a large glossary. However, some unreasonable hypotheses for acoustic modeling and the unclassified training algorithm on which the HMM based form a bottleneck, restricting the further improvement in speech recognition. The Artificial Neural Network (ANN) techniques can be adopted as an alternative modeling paradigm. By means of the weight values of the network connections, neural networks can steadily store the knowledge acquired from the training process. But they possess a weak memory, not being suitable to store the instantaneous response to various input modes. To overcome the flaws of the HMM paradigm, we design a hybrid HMM/ANN model. In this hybrid model. the nonparametric probabilistic model (a BP neural network) is used to substitute the Gauss blender to calculate the observed probability which is necessary for computing the states of the HMM model. To optimizing the network structure in and after the training process, we propose an algorithm to prune hidden nodes in a trained neural network, and utilize the generalized hebbian algorithm to reconfigure the parameters of the network. Some experiments show that the hybrid model has a good performance in speech recognition.

关键词： HMM ANN BP removing hidden nodes algorithm generalized hebbian algorithm

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A NEW HYBRID HMM/ANN MODEL FOR SPEECH RECOGNITION

A NEW HYBRID HMM/ANN MODEL FOR SPEECH RECOGNITION

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IFIP TC12 WG12.5-Second IFIP Conference on Artificial Intelligence Applications and Innovations(AIAI2005)

作者： Xiaojing Xi Kunhui Lin Changle Zhou Jun Cai Software School of XiaMen University Department of Computer Science XiaMen University

Because of the application of the Hidden Markov Model(HMM) in acoustic modeling,a significant breakthrough has been made in recognizing continuous speech with a large ***,some unreasonable hypotheses for acoustic modeling and the unclassified training algorithm on which the HMM based form a bottleneck,restricting the further improvement in speech *** Artificial Neural Network(ANN) techniques can be adopted as an alternative modeling *** means of the weight values of the network connections,neural networks can steadily store the knowledge acquired from the training *** they possess a weak memory,not being suitable to store the instantaneous response to various input *** overcome the flaws of the HMM paradigm,we design a hybrid HMM/ANN *** this hybrid model,the nonparametric probabilistic model(a BP neural network) is used to substitute the Gauss blender to calculate the observed probability which is necessary for computing the states of the HMM *** optimizing the network structure in and after the training process,we propose an algorithm to prune hidden nodes in a trained neural network,and utilize the generalized hebbian algorithm to reconfigure the parameters of the *** experiments show that the hybrid model has a good performance in speech recognition.

关键词： HMM ANN BP removing hidden nodes algorithm generalized hebbian algorithm

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Multistage infrared target detection

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OPTICAL ENGINEERING 2003年第9期42卷 2746-2754页

作者： Chan, AL Der, SZ Nasrabadi, NM USA Res Lab Attn AMSRL SE SE Adelphi MD 20783 USA

algorithms are considered for searching wide-area forward-looking infrared imagery for military vehicles. Wide-area search has typically been handled by using a simple detection algorithm with low computational cost to search the entire image or set of images, followed by a clutter rejection algorithm that analyzes only those portions of the image that are marked by the detection algorithm. We start with a feature-based detector and an eigen-neural-based clutter rejecter, and examine a number of architectures for combining these modules to maximize joint performance. The architectures considered include a clutter rejection threshold method and a nonlinear learning-based combination. The performance of the architectures is compared using a set of several thousand real images. (C) 2003 Society of Photo-Optical Instrumentation Engineers.

关键词： target recognition target detection clutter rejection FLIR imagery feature extraction generalized hebbian algorithm multilayer perceptron

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Automation of pattern recognition in cDNA microarray data: An application to gene-based diagnostic prediction of cancers

Automation of pattern recognition in cDNA microarray data: A...

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Conference on Vision Geometry XI

作者： Wang, LY Assadi, AH Univ Wisconsin Dept Biomed Engn Madison WI USA

ISBN: (纸本)0819445614

The purpose of this paper is two-fold: (a) to demonstrate that pattern recognition methods in image processing leads to a noticeable improvement in bioinformatics, specifically, analysis of microarray data(**) in gene expression correlated to cancer;(b) to bring the utility of geometric methods in pattern recognition to attention of researchers in bioinformatics and molecular biologists. Our method of analysis is seen to readily provide great improvement over the latest published methods in bioinformatics. We also hope that in the process, we provide mathematical insight into the problems of microarray data analysis from the point of view of signal processing and learning theory. Researchers have, to a great extent, successfully decoded the human genome. A work horse for gene expression profiling is cDNA microarray technology, an arsenal of tools that have been well developed in the last decade [1]. Recently, the first application of Artificial Neural Network (ANN) has been reported [2] that tackles diagnostic classification of cancer using gene expression data derived from cDNA microarrays. In this paper, we offer a geometric heuristics to systematize pattern recognition problems in microarray data, and show how to interpret this general scheme in the context of diagnostic prediction of cancer. More specifically, we show that a fruitful method for geometric linearization of microarray data is to use Principal Component Analysis (PCA) [5] to approximate the digital geometric dimension of the space of all cDNA microarray data with biological constraints imposed by search for cancer gene expression. Implementation through unsupervised learning (for example, using a generalized hebbian algorithm (GHA) [3,4]) extracts 10 principal directions in "the statistical tangent-space" of the data space. This approximation is sufficiently fine to allow a highly reliable automation of the pattern-recognition phase in gene-based diagnostic prediction of cancer. We offer a concrete

关键词： generalized hebbian algorithm neural network geometric methods microarray

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Neural networks for seismic principal components analysis

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1999年第1期37卷 297-311页

作者： Huang, KY Natl Chiao Tung Univ Dept Comp & Informat Sci Hsinchu 30050 Taiwan

The neural network, using an unsupervised generalized hebbian algorithm (GHA), is adopted to find the principal eigenvectors of a covariance matrix in different kinds of seismograms. We have shown that the extensive computer results of the principal components analysis (PCA) using the neural net of GHA can extract the information of seismic reflection layers and uniform neighboring traces. The analyzed seismic data are the seismic traces with 20-, 25-, and 30-Hz Ricker wavelets, the fault, the reflection and diffraction patterns after normal moveout (NMO) correction, the bright spot pattern, and the real seismogram at Mississippi Canyon. The properties of high amplitude, low frequency, and polarity reversal can be shown from the projections on the principal eigenvectors. For PCA, a theorem is proposed, which states that adding an extra point along the direction of the existing eigenvector can enhance that eigenvector. The theorem is applied to the interpretation of a fault seismogram and the uniform property of other seismograms. The PCA also provides a significant seismic data compression.

关键词： data compression eigenvectors generalized hebbian algorithm neural network principal component analysis (PCA) Ricker wavelets seismic interpretation

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CGHA for principal component extraction in the complex domain

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1997年第5期8卷 1031-1036页

作者： Zhang, YW Ma, YL NORTHWESTERN POLYTECH UNIV COLL MARINE ENGN XIAN 710072 PEOPLES R CHINA

Principal component extraction is an efficient statistical tool which is applied to data compression, feature extraction, signal processing, etc. Representative algorithms in the literature can only handle real data. However, in many scenarios such as sensor array signal processing, complex data are encountered. In this paper, the complex domain generalized hebbian algorithm (CGHA) is presented for complex principal component extraction. It extends the real domain generalized hebbian algorithm (GHA) proposed by Sanger. Convergence of CGHA is analyzed. Like GHA, CGHA can be implemented by a single-layer linear neural network with simple computation. An example is given where CGHA is utilized in direction-of-arrival (DOA) estimation of multiple narrowband plane waves received by a sensor array.

关键词： complex domain convergence direction-of-arrival estimation generalized hebbian algorithm neural network principal component single layer

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