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检索条件"任意字段=2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017"

共 1665 条记录，以下是61-70 订阅

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MULTINOMIAL SAMPLING FOR HIERARCHICAL CHANGE-POINT DETECTION 30

MULTINOMIAL SAMPLING FOR HIERARCHICAL CHANGE-POINT DETECTION

引用

30th ieee international workshop on machine learning for signal processing (mlsp)

作者： Romero-Medrano, Lorena Moreno-Munoz, Pablo Artes-Rodriguez, Antonio Univ Carlos III Madrid Dept Signal Theory & Commun Madrid Spain Gregorio Maranon Hlth Res Inst Madrid Spain

ISBN: (纸本)9781728166629

Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series. We assume that change-points lie on a lower-dimensional manifold where we aim to infer subsets of discrete latent variables. For this model, full inference is computationally unfeasible and pseudo-observations based on pointestimates are used instead. However, if estimation is not certain enough, change-point detection gets affected. To circumvent this problem, we propose a multinomial sampling methodology that improves the detection rate and reduces the delay while keeping complexity stable and inference analytically tractable. Our experiments show results that outperform the baseline method and we also provide an example oriented to a human behavior study.

关键词： Bayesian inference change-point detection (CPD) latent variable models multinomial likelihoods

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UNSUPERVISED learning OF MARKOV-SWITCHING STOCHASTIC VOLATILITY WITH AN APPLICATION TO MARKET DATA 26

UNSUPERVISED LEARNING OF MARKOV-SWITCHING STOCHASTIC VOLATIL...

引用

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

作者： Gorynin, Ivan Monfrini, Emmanuel Pieczynski, Wojciech Univ Paris Saclay CNRS Telecom SudParis SAMOVAR 9 Rue Charles Fourrier F-91000 Evry France

ISBN: (纸本)9781509007462

We introduce a new method for estimating the regime-switching stochastic volatility models from the historical prices. Our methodology is based on a novel version of the assumed density filter (ADF). We estimate the switching model by maximizing the quasi-likelihood function of our ADF. The simulation experiments show the efficiency of our method. Then we analyze different market price histories for consistency with a regime-shifting model.

关键词： Stochastic volatility Markov-switching stochastic volatility models Quasi-maximum likelihood Assumed density filtering Gaussian quadrature

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A PROXIMAL METHOD FOR THE K-SVD DICTIONARY learning

A PROXIMAL METHOD FOR THE K-SVD DICTIONARY LEARNING

引用

23rd ieee international workshop on machine learning for signal processing (mlsp)

作者： Peng, Guan-Ju Hwang, Wen-Liang Acad Sinica Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9781479911806

In this paper, we propose a dictionary updating method and show numerically that it can converge to a dictionary that outperforms the dictionary derived by the K-SVD method. The proposed method is based on the proximal point approach used in the convex optimization algorithm. We incorporate the approach into the well-known MOD and combine the result with the K-SVD method to obtain the proposed method. We analyze the complexity of the proposed method and compare it with that of the K-SVD method. The results of experiments demonstrate that our method outperforms K-SVD with only a slight increase in the execution time.

关键词： convex programming dictionaries learning (artificial intelligence) singular value decomposition

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learning WITH THE KERNEL signal TO NOISE RATIO

LEARNING WITH THE KERNEL SIGNAL TO NOISE RATIO

引用

22nd ieee international workshop on machine learning for signal processing (mlsp)

作者： Gomez-Chova, Luis Camps-Valls, Gustavo Univ Valencia Image Proc Lab E-46003 Valencia Spain

ISBN: (纸本)9781467310260

This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in nonlinear regression examples, dependence estimation and causal inference, nonlinear channel equalization, and nonlinear feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extracts more noise-free features when confronted with standard approaches.

关键词： Kernel methods signal to noise ratio feature extraction classification regression dependence estimation

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VISUALIZING AND IMPROVING SCATTERING NETWORKS

VISUALIZING AND IMPROVING SCATTERING NETWORKS

引用

27th ieee international workshop on machine learning for signal processing (mlsp)

作者： Cotter, Fergal Kingsbury, Nick Univ Cambridge Dept Engn Signal Proc Grp Cambridge England

ISBN: (纸本)9781509063413

Scattering Transforms (or ScatterNets) introduced by Mallat in [1] are a promising start into creating a well-defined feature extractor to use for pattern recognition and image classification tasks. They are of particular interest due to their architectural similarity to Convolutional Neural Networks (CNNs), while requiring no parameter learning and still performing very well (particularly in constrained classification tasks). In this paper we visualize what the deeper layers of a ScatterNet are sensitive to using a 'DeScatterNet'. We show that the higher orders of ScatterNets are sensitive to complex, edge-like patterns (checker-boards and rippled edges). These complex patterns may be useful for texture classification, but are quite dissimilar from the patterns visualized in second and third layers of Convolutional Neural Networks (CNNs) - the current state of the art Image Classifiers. We propose that this may be the source of the current gaps in performance between ScatterNets and CNNs (83% vs 93% on CIFAR-10 for ScatterNet+SVM vs ResNet). We then use these visualization tools to propose possible enhancements to the ScatterNet design, which show they have the power to extract features more closely resembling CNNs, while still being well-defined and having the invariance properties fundamental to ScatterNets.

关键词： ScatterNets DeScatterNets Scattering Network Convolutional Neural Network Visualization DTCWT

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learning EMBEDDINGS FOR SPEAKER CLUSTERING BASED ON VOICE EQUALITY

LEARNING EMBEDDINGS FOR SPEAKER CLUSTERING BASED ON VOICE EQ...

引用

27th ieee international workshop on machine learning for signal processing (mlsp)

作者： Lukic, Yanick X. Vogt, Carlo Duerr, Oliver Stadelmann, Thilo Zurich Univ Appl Sci Winterthur Switzerland

ISBN: (纸本)9781509063413

Recent work has shown that convolutional neural networks (CNNs) trained in a supervised fashion for speaker identification are able to extract features from spectrograms which can be used for speaker clustering. These features are represented by the activations of a certain hidden layer and are called embeddings. However, previous approaches require plenty of additional speaker data to learn the embedding, and although the clustering results are then on par with more traditional approaches using MFCC features etc., room for improvements stems from the fact that these embeddings are trained with a surrogate task that is rather far away from segregating unknown voices - namely, identifying few specific speakers. We address both problems by training a CNN to extract embeddings that are similar for equal speakers (regardless of their specific identity) using weakly labeled data. We demonstrate our approach on the well-known TIMIT dataset that has often been used for speaker clustering experiments in the past. We exceed the clustering performance of all previous approaches, but require just 100 instead of 590 unrelated speakers to learn an embedding suited for clustering.

关键词： Speaker Clustering Speaker Recognition Convolutional Neural Network Speaker Embedding

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MINI-BATCH STOCHASTIC APPROACHES FOR ACCELERATED MULTIPLICATIVE UPDATES IN NONNEGATIVE MATRIX FACTORISATIONWITH BETA-DIVERGENCE 26

MINI-BATCH STOCHASTIC APPROACHES FOR ACCELERATED MULTIPLICAT...

引用

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

作者： Serizel, Romain Essid, Slim Richard, Gael Univ Paris Saclay LTCI CNRS Telecom ParisTech F-75013 Paris France

ISBN: (纸本)9781509007462

Nonnegative matrix factorisation (NMF) with beta-divergence is a popular method to decompose real world data. In this paper we propose mini-batch stochastic algorithms to perform NMF efficiently on large data matrices. Besides the stochastic aspect, the mini-batch approach allows exploiting intensive computing devices such as general purpose graphical processing units to decrease the processing time and in some cases outperform coordinate descent approach.

关键词： Nonnegative matrix factorisation GPGPU multiplicative rules online learning

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DIFFUSION STRATEGIES FOR IN-NETWORK PRINCIPAL COMPONENT ANALYSIS 24

DIFFUSION STRATEGIES FOR IN-NETWORK PRINCIPAL COMPONENT ANAL...

引用

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

作者： Ghadban, Nisrine Honeine, Paul Mourad-Chehade, Farah Francis, Clovis Farah, Joumana Univ Technol Troyes CNRS Inst Charles Delaunay Troyes France Univ Libanaise Fac Genie Beirut Lebanon Holy Spirit Univ Kaslik Fac Engn Dept Telecommun Kaslik Lebanon

ISBN: (纸本)9781479936946

This paper deals with the principal component analysis in networks, where it is improper to compute the sample covariance matrix. To this end, we derive several in-network strategies to estimate the principal axes, including noncooperative and cooperative (diffusion-based) strategies. The performance of the proposed strategies is illustrated on diverse applications, including image processing and dimensionality reduction of time series in wireless sensor networks.

关键词： Principal component analysis network adaptive learning distributed processing

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A SUBSPACE learning ALGORITHM FOR MICROWAVE SCATTERING signal CLASSIFICATION WITH APPLICATION TO WOOD QUALITY ASSESSMENT

A SUBSPACE LEARNING ALGORITHM FOR MICROWAVE SCATTERING SIGNA...

引用

22nd ieee international workshop on machine learning for signal processing (mlsp)

作者： Yu, Yinan McKelvey, Tomas Chalmers Dept Signals & Syst S-41296 Gothenburg Sweden

ISBN: (纸本)9781467310260

A classification algorithm based on a linear subspace model has been developed and is presented in this paper. To further improve the classification results, the full linear subspace of each class is split into subspaces with lower dimensions and characterized by local coordinates constructed from automatically selected training data. The training data selection is implemented by optimizations with least squares constraints or L1 regularization. The working application is to determine the quality in wooden logs using microwave signals [1]. The experimental results are shown and compared with classical methods.

关键词： classification linear subspace sparse representation training data selection

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A CONSENSUS-BASED DECENTRALIZED EM FOR A MIXTURE OF FACTOR ANALYZERS 24

A CONSENSUS-BASED DECENTRALIZED EM FOR A MIXTURE OF FACTOR A...

引用

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

作者： Whipps, Gene T. Ertin, Emre Moses, Randolph L. Ohio State Univ ECE Dept Columbus OH 43210 USA

ISBN: (纸本)9781479936946

We consider the problem of decentralized learning of a target appearance manifold using a network of sensors. Sensor nodes observe an object from different aspects and then, in an unsupervised and distributed manner, learn a joint statistical model for the data manifold. We employ a mixture of factor analyzers (MFA) model, approximating a potentially nonlinear manifold. We derive a consensus-based decentralized expectation maximization (EM) algorithm for learning the parameters of the mixture densities and mixing probabilities. A simulation example demonstrates the efficacy of the algorithm.

关键词： decentralized learning Gaussian mixture mixture of factor analyzers consensus

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