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2010 IEEE International Workshop on Machine Learning for Signal Processing

Aug. 29 2010-Sept. 1 2010

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Displaying Results 1 - 25 of 91
  • [Front cover]

    Publication Year: 2010, Page(s): c1
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  • [Title page]

    Publication Year: 2010, Page(s): i
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  • [Copyright notice]

    Publication Year: 2010, Page(s): ii
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  • MLSP 2010 paper index

    Publication Year: 2010, Page(s):iii - viii
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  • Preface

    Publication Year: 2010, Page(s): ix
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  • Acknowledgements

    Publication Year: 2010, Page(s): x
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  • MLSP 2010 programme Committee

    Publication Year: 2010, Page(s): xi
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  • MLSP 2010 organising Committee

    Publication Year: 2010, Page(s): xii
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  • MLSP 2010 author index

    Publication Year: 2010, Page(s):474 - 476
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  • Mutual information based dimensionality reduction with application to non-linear regression

    Publication Year: 2010, Page(s):1 - 6
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1029 KB) | HTML iconHTML

    In this paper we introduce a supervised linear dimensionality reduction algorithm which is based on finding a projected input space that maximizes mutual information between input and output values. The algorithm utilizes the recently introduced MeanNN estimator for differential entropy. We show that the estimator is an appropriate tool for the dimensionality reduction task. Next we provide a nonl... View full abstract»

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  • Functional data representation using correntropy locally linear embedding

    Publication Year: 2010, Page(s):7 - 12
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (272 KB) | HTML iconHTML

    Unlike classical linear dimensionality reduction techniques, nonlinear ones are capable of discovering the nonlinear degrees of freedom that are present in natural observations, by assuming that the data lie on an embedded nonlinear manifold within an observed high dimensional feature space. Nevertheless, when measured objects are actually functional data, nonlinear dimensionality reduction techni... View full abstract»

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  • Local dimensionality reduction for multiple instance learning

    Publication Year: 2010, Page(s):13 - 18
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1116 KB) | HTML iconHTML

    Multiple instance learning involves labeling bags (sets of instances) rather than individual instances. Positive bags contain both true positive and false positive instances, leading to label ambiguity, while negative bags consist of only true negative instances. Since labels for individual instances are not known, a direct application of existing discriminant analysis or dimensionality reduction ... View full abstract»

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  • Multiplicative updates for t-SNE

    Publication Year: 2010, Page(s):19 - 23
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (167 KB) | HTML iconHTML

    It has been demonstrated that Student t-Distributed Stochastic Neighbor Embedding (t-SNE) can enhance discovery of clusters of data. However, the original t-SNE implementation employs an additive gradient-based algorithm which requires suitable learning step size and momentum rate, the tuning of which can be laborious. We propose a novel fixed-point algorithm that overcomes such parameter selectio... View full abstract»

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  • Manifold-respecting probabilistic matrix tri-factorization

    Publication Year: 2010, Page(s):24 - 28
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (416 KB) | HTML iconHTML

    Probabilistic latent semantic analysis (PLSA) is a popular topic model for factor analysis of dyadic data, which is closely related to nonnegative matrix factorization (NMF) that seeks a 2-factor decomposition of a nonnegative data matrix. We previously proposed probabilistic matrix tri-factorization (PMTF) which is a probabilistic model for a 3-factor decomposition of a nonnegative data matrix, e... View full abstract»

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  • Single-frame image super-resolution using a Pearson type VII MRF

    Publication Year: 2010, Page(s):29 - 34
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1092 KB) | HTML iconHTML

    Image super-resolution restoration aims to recover a high resolution scene from its low resolution measurements. It is a difficult, ill-posed problem, with no consensus as to how best to formulate image models that can both impose smoothness and preserve the edges in the image. Here we develop a new image prior based on the Pearson type VII density integrated with a Markov Random Field model. This... View full abstract»

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  • A one-pass resource-allocating codebook for patch-based visual object recognition

    Publication Year: 2010, Page(s):35 - 40
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (136 KB) | HTML iconHTML

    Frequencies of occurrence of low-level image features is the representation of choice in the design of state-of-the-art visual object recognition systems. A crucial step in this process is the construction of a codebook of visual features, which is usually done by cluster analysis of a large number of low-level image features detected as interest points. However, clustering is a process that retai... View full abstract»

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  • Learning spatial filters for multispectral image segmentation

    Publication Year: 2010, Page(s):41 - 46
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1461 KB) | HTML iconHTML

    We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. ... View full abstract»

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  • Improperness measures for quaternion random vectors

    Publication Year: 2010, Page(s):47 - 52
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (367 KB) | HTML iconHTML

    It has been recently proved that the two main kinds of quaternion improperness require two different kinds of widely linear processing. In this work, we show that these definitions satisfy some important properties, which include the invariance to quaternion linear transformations and right Clifford translations, as well as some clear connections with the case of proper complex vectors. Moreover, ... View full abstract»

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  • Bayesian BCJR for channel equalization and decoding

    Publication Year: 2010, Page(s):53 - 58
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (402 KB) | HTML iconHTML

    In this paper we focus on the probabilistic channel equalization in digital communications. We face the single input single output (SISO) model to show how the statistical information about the multipath channel can be exploited to further improve our estimation of the a posteriori probabilities (APP) during the equalization process. We consider not only the uncertainty due to the noise in the cha... View full abstract»

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  • Reinforcement learning method for energy efficient cooperative multiband spectrum sensing

    Publication Year: 2010, Page(s):59 - 64
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (449 KB) | HTML iconHTML

    Cognitive radios (CR) and dynamic spectrum access (DSA) attempt to exploit the underutilized radio spectrum by allowing secondary users to access the licensed frequencies in an opportunistic manner. In order to avoid collisions with the primary user the secondary users need to sense the spectrum, and to mitigate the effects of channel fading on sensing cooperative schemes have been proposed in the... View full abstract»

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  • Error-related potential recorded by EEG in the context of a p300 mind speller brain-computer interface

    Publication Year: 2010, Page(s):65 - 70
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (406 KB) | HTML iconHTML

    The Mind Speller® is a Brain-Computer Interface (BCI) which enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms (EEG). This BCI application is of particular interest for disabled patients who have lost all means of verbal and motor communication. Error-related Potentials (ErrP) in the EEG are generated by t... View full abstract»

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  • Removal of ballistocardiogram artifacts exploiting second order cyclostationarity

    Publication Year: 2010, Page(s):71 - 76
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4104 KB) | HTML iconHTML

    Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is increasingly used to monitor the brain activity. The interactions between the scanner magnetic field, the patient's body, and the EEG electrodes generate a pulsation artifact called ballistocardiogram (BCG) which is synchronized with the patient's heart beat. The BCG artifact is considered he... View full abstract»

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  • A constrained NMF algorithm for bold detection in fMRI

    Publication Year: 2010, Page(s):77 - 82
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (210 KB) | HTML iconHTML

    In this paper the application of Nonnegative Matrix Factorization (NMF) to Functional Magnetic Resonance Images (fMRIs) is addressed. We attempt to blindly separate the sources of fMRI mixtures. However, our interest is to find only one particular source (task-related source), which indicates the active area in the brain. We utilize the prior knowledge about time course of the corresponding source... View full abstract»

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  • Sparse nonnegative matrix factorization using ℓ0-constraints

    Publication Year: 2010, Page(s):83 - 88
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (380 KB) | HTML iconHTML

    Although nonnegative matrix factorization (NMF) favors a part-based and sparse representation of its input, there is no guarantee for this behavior. Several extensions to NMF have been proposed in order to introduce sparseness via the ℓ1-norm, while little work is done using the more natural sparseness measure, the ℓ0-pseudo-norm. In this work we propose two NMF... View full abstract»

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  • Non-negative matrix factorization for parameter estimation in hidden Markov models

    Publication Year: 2010, Page(s):89 - 94
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (433 KB) | HTML iconHTML

    Hidden Markov models are well-known in analysis of random processes, which exhibit temporal or spatial structure and have been successfully applied to a wide variety of applications such as but not limited to speech recognition, musical scores, handwriting, and bio-informatics. We present a novel algorithm for estimating the parameters of a hidden Markov model through the application of a non-nega... View full abstract»

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