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检索条件"任意字段=21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011"

共 73 条记录，以下是1-10 订阅

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ieee international workshop on machine learning for signal processing: Preface

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

引用

21st ieee international workshop on machine learning for signal processing, mlsp 2011

作者： Tan, Tieniu Katagiri, Shigeru Tao, Jianhua Nakamura, Atsushi Larsen, Jan

ISBN: (纸本)9781457716232

The 21st ieee international workshop on machine learning for signal processing will be held in Beijing, China, on September 18-21, 2011. The workshop series is the major annual technical event of the ieee signal processing Society's Technical Committee on machine learning for signal processing. This year the workshop is held in the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences. © 2011 ieee.

关键词： learning systems

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2011 ieee international workshop on machine learning for signal processing - Proceedings of mlsp 2011

2011 IEEE International Workshop on Machine Learning for Sig...

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21st ieee international workshop on machine learning for signal processing, mlsp 2011

ISBN: (纸本)9781457716232

The proceedings contain 96 papers. The topics discussed include: protein subcellular localization prediction based on profile alignment and gene ontology;a sinusoidal audio and speech analysis/synthesis model based on improved EMD by adding pure tone;data representation and feature selection for colorimetric sensor arrays used as explosives detectors;efficient preference learning with pairwise continuous observations and Gaussian processes;active one-class learning by kernel density estimation;large scale topic modeling made practical;underdetermined convolutive blind source separation using a novel mixing matrix estimation and MMSE-based source estimation;robust online estimation of the vanishing point for vehicle mounted cameras;Gaussian process for human motion modeling: a comparative study;multi-resolution inversion algorithm for the attenuated radon transform;and a reproducing kernel Hilbert space formulation of the principle of relevant information.

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KERNEL ENTROPY COMPONENT ANALYSIS: NEW THEORY AND SEMI-SUPERVISED learning

KERNEL ENTROPY COMPONENT ANALYSIS: NEW THEORY AND SEMI-SUPER...

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21st ieee international workshop on machine learning for signal processing (mlsp)

作者： Jenssen, Robert Univ Tromso Dept Phys & Technol N-9001 Tromso Norway

ISBN: (纸本)9781457716232

A new theory for kernel entropy component analysis (kernel ECA) is developed, based on distribution dependent convolution operators, ensuring the validity of the method for any positive semi-definite kernel. Furthermore, a new semi-supervised kernel ECA classification method is derived with positive results compared to the state-of-the-art.

关键词： Kernel entropy component analysis convolution operators spectral semi-supervised classification

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COOPERATIVE DATA CENSORING FOR ENERGY-EFFICIENT COMMUNICATIONS IN SENSOR NETWORKS

COOPERATIVE DATA CENSORING FOR ENERGY-EFFICIENT COMMUNICATIO...

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21st ieee international workshop on machine learning for signal processing (mlsp)

作者： Fernandez-Bes, Jesus Arroyo-Valles, Rocio Cid-Sueiro, Jesus Univ Carlos III Madrid Signal Theory & Commun Dept Leganes 28911 Spain

ISBN: (纸本)9781457716232

signal processing algorithms in Wireless Sensor Networks claim for energy efficiency because of node energy scarcity. Tailored to this scenario, in this paper we develop energy-efficient cooperative strategies for selective communications. Cooperation among nodes is exploited in order to optimize energy consumption while guaranteeing good overall performance. The analysis of representative scenarios reveals that cooperative selective nodes yield a good performance in both network lifetime and quality of the transmitted information under different network conditions.

关键词： Energy efficiency

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METHODS FOR learning ADAPTIVE DICTIONARY IN UNDERDETERMINED SPEECH SEPARATION

METHODS FOR LEARNING ADAPTIVE DICTIONARY IN UNDERDETERMINED ...

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21st ieee international workshop on machine learning for signal processing (mlsp)

作者： Xu, Tao Wang, Wenwu Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 5XH Surrey England

ISBN: (纸本)9781457716232

Underdetermined speech separation is a challenging problem that has been studied extensively in recent years. A promising method to this problem is based on the so-called sparse signal representation. Using this technique, we have recently developed a multi-stage algorithm, where the source signals are recovered using a pre-defined dictionary obtained by e.g. the discrete cosine transform (DCT). In this paper, instead of using the pre-defined dictionary, we present three methods for learning adaptive dictionaries for the reconstruction of source signals, and compare their performance with several state-of-the-art speech separation methods.

关键词： Underdetermined blind speech separation sparse representation adaptive dictionary learning

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DIMENSIONALITY REDUCTION FOR EEG CLASSIFICATION USING MUTUAL INFORMATION AND SVM

DIMENSIONALITY REDUCTION FOR EEG CLASSIFICATION USING MUTUAL...

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21st ieee international workshop on machine learning for signal processing (mlsp)

作者： Guerrero-Mosquera, Carlos Verleysen, Michel Navia Vazquez, Angel Univ Carlos III Madrid Signal Proc & Commun Dept Avda Univ 30 Leganes 28911 Spain Catholic Univ Louvain Machine Learning Grp B-1348 Louvain Belgium

ISBN: (纸本)9781457716232

Dimensionality reduction is a well known technique in signal processing oriented to improve both the computational cost and the performance of classifiers. We use an electroencephalogram (EEG) feature matrix based on three extraction methods: tracks extraction, wavelets coefficients and Fractional Fourier Transform. The dimension reduction is performed by Mutual Information (MI) and a forward-backward procedure. Our results show that feature extraction and dimension reduction could be considered as a new alternative for solving EEG classification problems.

关键词： dimensionality reduction Electroencephalography Support Vector Network Classifiers Feature extraction eeg feature signal processing dimensions

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TWO stAGE CLASSIFIER CHAIN ARCHITECTURE FOR EFFICIENT PAIR-WISE MULTI-LABEL learning

TWO STAGE CLASSIFIER CHAIN ARCHITECTURE FOR EFFICIENT PAIR-W...

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21st ieee international workshop on machine learning for signal processing (mlsp)

作者： Gjorgjevikj, Dejan Madjarov, Gjorgji Ss Cyril & Methodius Univ FEEIT Skopje North Macedonia

ISBN: (纸本)9781457716232

A common approach for solving multi-label learning problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning problems with large number of labels. To tackle this problem we propose a Two stage Classifier Chain Architecture (TSCCA) for efficient pair-wise multi-label learning. Six different real-world datasets were used to evaluate the performance of the TSCCA. The performance of the architecture was compared with six methods for multi-label learning and the results suggest that the TSCCA outperforms the concurrent algorithms in terms of predictive accuracy. In terms of testing speed TSCCA shows better performance comparing to the pair-wise methods for multi-label learning.

关键词： Multi-label learning two stage architecture classifier chains

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COLLABORATIVE learning OF MIXTURE MODELS USING DIFFUSION ADAPTATION

COLLABORATIVE LEARNING OF MIXTURE MODELS USING DIFFUSION ADA...

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21st ieee international workshop on machine learning for signal processing (mlsp)

作者： Towfic, Zaid J. Chen, Jianshu Sayed, Ali H. Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA

ISBN: (纸本)9781457716232

In large ad-hoc networks, classification tasks such as spam filtering, multi-camera surveillance, and advertising have been traditionally implemented in a centralized manner by means of fusion centers. These centers receive and process the information that is collected from across the network. In this paper, we develop a decentralized adaptive strategy for information processing and apply it to the task of estimating the parameters of a Gaussian-mixture-model (GMM). The proposed technique employs adaptive diffusion algorithms that enable adaptation, learning, and cooperation at local levels. The simulation results illustrate how the proposed technique outperforms non-collaborative learning and is competitive against centralized solutions.

关键词： online-learning Newton's method diffusion Expectation-Maximization Gaussian-mixture-model machine learning distributed processing

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BAYESIAN FEATURE SELECTION FOR SPARSE TOPIC MODEL

BAYESIAN FEATURE SELECTION FOR SPARSE TOPIC MODEL

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21st ieee international workshop on machine learning for signal processing (mlsp)

作者： Chang, Ying-Lan Lee, Kuen-Feng Chien, Jen-Tzung Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781457716232

This paper presents a new Bayesian sparse learning approach to select salient lexical features and build sparse topic model (stM). The Bayesian learning is performed by incorporating the spike-and-slab priors so that the words with spiky distributions are filtered and those with slab distributions are selected as features for estimating the topic model (TM) based on latent Dirichlet allocation. The variational inference procedure is developed to train stM parameters. In the experiments on document modeling using TM and stM, we find that the proposed stM does not only reduce the model perplexity but also reduce the memory and computation costs. Bayesian feature selection method does effectively identify the representative topic words for building a sparse learning model.

关键词： sparse features feature selection topic model Bayesian learning

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MODELING MUSICOLOGICAL INFORMATION AS TRIGRAMS IN A SYstEM FOR SIMULTANEOUS CHORD AND LOCAL KEY EXTRACTION

MODELING MUSICOLOGICAL INFORMATION AS TRIGRAMS IN A SYSTEM F...

引用

21st ieee international workshop on machine learning for signal processing (mlsp)

作者： Pauwels, Johan Martens, Jean-Pierre Leman, Marc Univ Ghent Digital Speech & Signal Proc Grp ELIS DSSP Ghent Belgium Univ Ghent Inst Psychoacoust & Elect Mus IPEM Ghent Belgium

ISBN: (纸本)9781457716232

In this paper, we discuss the introduction of a trigram musicological model in a simultaneous chord and local key extraction system. By enlarging the context of the musicological model, we hoped to achieve a higher accuracy that could justify the associated higher complexity and computational load of the search for the optimal solution. Experiments on multiple data sets have demonstrated that the trigram model has indeed a larger predictive power (a lower perplexity). This raised predictive power resulted in an improvement in the key extraction capabilities, but no improvement in chord extraction when compared to a system with a bigram musicological model.

关键词： Chord extraction key extraction music signal processing music information retrieval

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