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检索条件"任意字段=2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014"

共 15 条记录，以下是1-10 订阅

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thE 10th ANNUAL mlsp COMPETITION: thIRD PLACE 24

THE 10TH ANNUAL MLSP COMPETITION: THIRD PLACE

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ieee international workshop on machine learning for signal processing (mlsp)

作者： Koncevicius, Karolis Vilnius Univ Inst Math & Informat Vilnius Lithuania

the goal of the mlsp 2014 competition was to automatically detect subjects with schizophrenia based on multimodal features derived from magnetic resonance imaging data. this report summarizes the 3rd place solution wi... 详细信息

ISBN: (纸本)9781479936946

关键词： MRI Schizophrenia Classification Distance Weighted Discrimination

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signal stochastic decomposition over continuous dictionaries 24

Signal stochastic decomposition over continuous dictionaries

引用

2014 24th ieee international workshop on machine learning for signal processing, mlsp 2014

作者： Naulet, Zacharie Barat, Éric Gif-sur-YvetteF-91191 France

ISBN: (纸本)9781479936946

We propose a Bayesian nonparametrics method, including algorithm for posterior computation, for the sparse regression problem. Our method applies in a general setting, when there are direct or indirect noisy observations of the signal. We try to make a wide focus on smoothness properties and sparsity of the approximate. As an example, we consider the ill-posed inverse problem of Quantum Homodyne Tomography. © 2014 ieee.

关键词： Stochastic systems

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thE 10th ANNUAL mlsp COMPETITION: FIRST PLACE 24

THE 10TH ANNUAL MLSP COMPETITION: FIRST PLACE

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ieee international workshop on machine learning for signal processing (mlsp)

作者： Solin, Arno Sarkka, Simo Aalto Univ Dept Biomed Engn & Computat Sci Rakentajanaukio 2 C FI-02150 Espoo Finland

ISBN: (纸本)9781479936946

the goal of the mlsp 2014 Schizophrenia Classification Challenge was to automatically diagnose subjects with schizophrenia based on multimodal features derived from their magnetic resonance imaging (MRI) brain scans. this challenge took place between June 5 and July 20, 2014, and was organized on Kaggle. We present how this classification problem can be solved in terms of a Bayesian machine learning paradigm known as Gaussian process (GP) classification. the proposed solution achieved an AUC score of 0.928, and it ranked first on the Kaggle private leaderboard.

关键词： Schizophrenia magnetic resonance imaging Gaussian process classification

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Diffusion LMS for multitask problems with overlapping hypothesis subspaces 24

Diffusion LMS for multitask problems with overlapping hypoth...

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2014 24th ieee international workshop on machine learning for signal processing, mlsp 2014

作者： Chen, Jie Richard, Cedric Hero, Alfred O. Sayed, Ali H. University of Michigan Ann Arbor United States Université de Nice Sophia-Antipolis CNRS France University of California Los Angeles United States

ISBN: (纸本)9781479936946

there are many important applications that are multitask-oriented in the sense that there are multiple optimum parameter vectors to be inferred simultaneously by networked agents. In this paper, we formulate an online multitask learning problem where node hypothesis spaces partly overlap. A cooperative algorithm based on diffusion adaptation is derived. Some results on its stability and convergence properties are also provided. Simulations are conducted to illustrate the theoretical results. © 2014 ieee.

关键词： learning systems

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thE 10th ANNUAL mlsp COMPETITION: SECOND PLACE 24

THE 10TH ANNUAL MLSP COMPETITION: SECOND PLACE

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ieee international workshop on machine learning for signal processing (mlsp)

作者： Lebedev, Alexander V. Univ Bergen Dept Clin Med Bergen Norway Stavanger Univ Hosp Ctr Age Related Med Stavanger Norway

ISBN: (纸本)9781479936946

the goal of the mlsp 2014 Classification Challenge was to automatically detect subjects with schizophrenia and schizoaffective disorder based on multimodal features derived from the magnetic resonance imaging (MRI) data. the patients with age range of 18-65 years were diagnosed according to DSM-IV criteria. the training data consisted of 46 patients and 40 healthy controls. the test set included 119 748 subjects with unknown labels. In the present solution, we implemented so-called "feature trimming", consisting of: 1) introducing a random vector into the feature set, 2) calculating feature importance based on mean decrease of the Gini-index derived by running Random Forest classification, and 3) removing the features with importance below the "dummy variable". Support Vector machine with Gaussian Kernel was used to run final classification with reduced feature set achieving test set AUC of 0.923.

关键词： Schizophrenia MRI Random Forest Support Vector machines Feature Trimming

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ieee international workshop on machine learning for signal processing, mlsp

IEEE International Workshop on Machine Learning for Signal P...

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2014 24th ieee international workshop on machine learning for signal processing, mlsp 2014

ISBN: (纸本)9781479936946

the proceedings contain 89 papers. the topics discussed include: mahalanobis-based one-class classification;improving the robustness of surface enhanced Raman spectroscopy based sensors by Bayesian non-negative matrix factorization;data mining by nonnegative tensor approximation;non-negative tensor factorization with missing data for the modeling of gene expressions in the human brain;multiple speaker tracking with the factorial von mises-fisher filter;a probabilistic approach to hearing loss compensation;coherent time modeling of semi-Markov models with application to real-time audio-to-score alignment;ultra-low-power voice-activity-detector through context and resource-cost-aware feature selection in decision trees;and a probabilistic approach for phase estimation in single-channel speech enhancement using von mises phase priors.

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BAYESIAN learning FOR SPEECH DEREVERBERATION 26

BAYESIAN LEARNING FOR SPEECH DEREVERBERATION

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26th ieee international workshop on machine learning for signal processing (mlsp)

作者： Chien, Jen-Tzung Chang, You-Cheng Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu Taiwan

ISBN: (纸本)9781509007462

this study presents a Bayesian approach to enhance the magnitude spectra of single-channel reverberant speech signals. Speech dereverberation model is constructed by using a non-negative convolutive transfer function (NCTF) and a nonnegative matrix factorization (NMF). NCTF is used to characterize the magnitude spectra of speech signal and room impulse response while NMF is applied to represent the fine structure of speech spectra. Importantly, we deal with the variations of dereverberation model by introducing the exponential priors for reverberation kernel and noise signal. A full Bayesian solution to speech dereverberation is obtained according to the variational Bayesian inference algorithm. Using this algorithm, the room configuration and the speaker characteristics are automatically learned from data. Such a general model can be reduced to the previous methods. Experimental results on both simulated data and real recordings from 2014 RE-VERB Challenge show the merit of the proposed method for single-channel speech dereverberation.

关键词： Bayesian learning nonnegative matrix factorization speech dereverberation

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ON thE RELATIONSHIP BETWEEN ONLINE GAUSSIAN PROCESS REGRESSION AND KERNEL LEAST MEAN SQUARES ALGORIthMS 26

ON THE RELATIONSHIP BETWEEN ONLINE GAUSSIAN PROCESS REGRESSI...

引用

26th ieee international workshop on machine learning for signal processing (mlsp)

作者： Van Vaerenbergh, Steven Fernandez-Bes, Jesus Elvira, Victor Univ Cantabria Dept Commun Engn Cantabria Spain CIBER BBN Zaragoza Spain Univ Zaragoza IIS Aragon I3A BSICoS Grp Zaragoza Spain Univ Carlos III Madrid Dept Signal Theory & Commun E-28903 Getafe Spain

ISBN: (纸本)9781509007462

We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms. While the latter have no capacity of storing the entire posterior distribution during online learning, we discover that their operation corresponds to the assumption of a fixed posterior covariance that follows a simple parametric model. Interestingly, several well-known KLMS algorithms correspond to specific cases of this model. the probabilistic perspective allows us to understand how each of them handles uncertainty, which could explain some of their performance differences.

关键词： online learning regression Gaussian processes kernel least-mean squares

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A BLOCK-BASED COMBINED SCHEME EXPLOITING SPARSITY IN NONLINEAR ACOUSTIC ECHO CANCELLATION 26

A BLOCK-BASED COMBINED SCHEME EXPLOITING SPARSITY IN NONLINE...

引用

26th ieee international workshop on machine learning for signal processing (mlsp)

作者： Comminiello, Danilo Scarpiniti, Michele Azpicueta-Ruiz, Luis A. Arenas-Garcia, Jeronimo Uncini, Aurelio Sapienza Univ Rome Dept Inform Engn Elect & Telecom I-00184 Rome Italy Univ Carlos III Madrid Dept Signal Theory & Communic Leganes 28911 Spain

ISBN: (纸本)9781509007462

Nonlinear acoustic echo cancellation (NAEC) aims at estimating both the acoustic impulse response and the nonlinearities affecting the desired signal. Both the modeling processes show behaviors of sparse nature from an energy point of view. In this paper, we propose an adaptive NAEC algorithm that takes advantage of such sparsity behaviors to improve echo cancellation performance. the proposed scheme is characterized by two block-based adaptive combinations of proportionate adaptive filters, having different strategies, devoted respectively to the estimation of the linear and nonlinear responses. the proposed model is assessed in NAEC problems, where its advantages and effectiveness are shown.

关键词： Sparse Adaptive Filters Adaptive Combination of Filters Linear-in-the-Parameters Nonlinear Filters Nonlinear Adaptive Filtering Functional Links

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3D HOUGH TRANSFORM-BASED LEFT VENTRICLE 3D OBJECT CLASSIFICATION FOR CARDIOMYOPAthY DIAGNOSIS 34

3D HOUGH TRANSFORM-BASED LEFT VENTRICLE 3D OBJECT CLASSIFICA...

引用

34th international workshop on machine learning for signal processing

作者： Goncalves, Vagner Mendonca Bergamasco, Leila C. C. Nunes, Fatima L. S. Univ Sao Paulo Escola Artes Ciencias & Humanidades Lab Comp Applicat Hlth Care Sao Paulo SP Brazil Inst Fed Educ Ciencia & Tecnol Sao Paulo Sao Paulo SP Brazil Ctr Univ FEI Sao Bernardo Do Campo SP Brazil

ISBN: (纸本)9798350372267;9798350372250

Cardiomyopathies are diseases characterized by anomalies in the myocardium that in most cases mainly affect the left ventricle of the heart. the progression of these diseases can lead to heart failure and arrhythmias, as well as increase the risk of sudden death. Cardiac Magnetic Resonance Imaging (CMRI) is an important tool for the diagnosis of these diseases. However, a CMRI exam produces dozens of images over a period of time that need to be visually analyzed by the specialist to compose a diagnosis. this task is exhaustive and can often lead to visual fatigue, compromising the accuracy of the diagnosis. In this research, we investigated the application of a 3D Hough Transform-based feature descriptor, presented in a previous work, combined with Supervised machine learning algorithms to classify left ventricle 3D objects reconstructed from CMRI slices. In terms of F1-score and accuracy, we observed overall mean classification performance of up to 0.75. In terms of AUC, the overall mean performance reached 0.89, indicating promising results with high potential of application.

关键词： 3D Hough Transform computer-aided diagnosis dilated cardiomyopathy hypertrophic cardiomyopathy supervised machine learning

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