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43 篇 会议

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检索条件"任意字段=2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012"

共 43 条记录，以下是1-10 订阅

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THE EIGHTH ANNUAL mlsp COMPETITION: SECOnd PLACE TEAM

THE EIGHTH ANNUAL MLSP COMPETITION: SECOND PLACE TEAM

引用

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

作者： Huttunen, Heikki Erkkila, Timo Ruusuvuori, Pekka Manninen, Tapio Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)9781467310260

This paper describes our submission to the eighth annual mlsp competition organized by Amazon during the 2012 ieee mlsp workshop. Our approach is based on a nearest-neighbor-like classifier with a distance metric learned from samples. The method was second in the final standings with prediction accuracy of 81 %, while the winning submission was 87 % accurate.

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NEAREST NEIGHBOR-BASED IMPORTANCE WEIGHTING

NEAREST NEIGHBOR-BASED IMPORTANCE WEIGHTING

引用

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

作者： Loog, Marco Delft Univ Technol Pattern Recognit Lab NL-2600 AA Delft Netherlands

ISBN: (纸本)9781467310260

Importance weighting is widely applicable in machine learning in general and in techniques dealing with data co-variate shift problems in particular. A novel, direct approach to determine such importance weighting is presented. It relies on a nearest neighbor classification scheme and is relatively straightforward to implement. Comparative experiments on various classification tasks demonstrate the effectiveness of our so-called nearest neighbor weighting (NNeW) scheme. Considering its performance, our procedure can act as a simple and effective baseline method for importance weighting.

关键词： learning systems

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REDUndANT TIME-FREQUENCY MARGINALS FOR CHIRPLET DECOMPOSITION

REDUNDANT TIME-FREQUENCY MARGINALS FOR CHIRPLET DECOMPOSITIO...

引用

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

作者： Weruaga, Luis Khalifa Univ Sharjah U Arab Emirates

ISBN: (纸本)9781467310260

This paper presents the foundations of a novel method for chirplet signal decomposition. In contrast to basis-pursuit techniques on over-complete dictionaries, the proposed method uses a reduced set of adaptive parametric chirplets. The estimation criterion corresponds to the maximization of the likelihood of the chirplet parameters from redundant time-frequency marginals. The optimization algorithm that results from this scenario combines Gaussian mixture models and Huber's robust regression in an iterative fashion. Simulation results support the proposed avenue.

关键词： Complex Gaussian chirplet sparse signal decomposition maximum likelihood

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MIXTURE WEIGHT INFLUENCE ON KERNEL ENTROPY COMPONENT ANALYSIS And SEMI-SUPERVISED learning USING THE LASSO

MIXTURE WEIGHT INFLUENCE ON KERNEL ENTROPY COMPONENT ANALYSI...

引用

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

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

ISBN: (纸本)9781467310260

The aim of this paper is two-fold. First, we show that the newly developed spectral method known as kernel entropy component analysis (kernel ECA) captures cluster structure, which is very important in semi-supervised learning, and we provide an analysis showing how mixture weights influence kernel ECA in a mixture of cluster components setting. Second, we develop a semi-supervised kernel ECA classifier based on the Lasso framework, and report promising results compared to the state-of-the art.

关键词： Spectral dimensionality reduction Semi-supervised learning Kernel entropy component analysis Cluster assumption Mixture densities Lasso Data spectroscopy

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LONG TERM HUMAN ACTIVITY RECOGNITION WITH AUTOMATIC ORIENTATION ESTIMATION

LONG TERM HUMAN ACTIVITY RECOGNITION WITH AUTOMATIC ORIENTAT...

引用

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

作者： Florentino-Liano, Blanca O'Mahony, Niamh Artes-Rodriguez, Antonio Univ Carlos III Madrid Dept Signal Theory & Commun Leganes 28911 Spain

ISBN: (纸本)9781467310260

This work deals with the elimination of sensitivity to sensor orientation in the task of human daily activity recognition using a single miniature inertial sensor. The proposed method detects time intervals of walking, automatically estimating the orientation in these intervals and transforming the observed signals to a "virtual" sensor orientation. Classification results show that excellent performance, in terms of both precision and recall (up to 100%), is achieved, for long-term recordings in real-life settings.

关键词： Activity recognition wearable sensors orientation estimation

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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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CLASSIFIER-BASED AFFINITIES FOR CLUSTERING SETS OF VECTORS

CLASSIFIER-BASED AFFINITIES FOR CLUSTERING SETS OF VECTORS

引用

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

作者： Garcia-Garcia, Dario Santos-Rodriguez, Raul Parrado-Hernandez, Emilio TECNALIA Res & Innovat Ind Syst Unit Donostia San Sebastian Spain Univ Carlos III Madrid Signal Theory & Commun Dpt Madrid Spain

ISBN: (纸本)9781467310260

We focus on the task of clustering sets of vectors. This can be seen as a special case of sequence clustering when the dynamics are not taken into account. We propose to use the error probability of binary classifiers to obtain a measure of the affinity between two sets so that a standard similarity-based clustering algorithm can be applied.

关键词： Sequence clustering speaker clustering sets of vectors

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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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ON SURROGATE SUPERVISION MULTIVIEW learning

ON SURROGATE SUPERVISION MULTIVIEW LEARNING

引用

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

作者： Jin, Gaole Raich, Raviv Oregon State Univ Sch EECS Corvallis OR 97331 USA

ISBN: (纸本)9781467310260

In semi-supervised multi-view learning, the input vector is partitioned into two views and a classifier based on each view is sought after. In such settings, often examples which include the two views and a label are available [1]. In this paper, we are interested in the setting where a classifier for examples from one view is sought after although no labeled examples are provided for that view. Specifically, we consider the setting where labeled examples are provided only for the other view along with additional unlabeled examples of the two views jointly. To solve this problem, we present the Classification-Constrained Canonical Correlation Analysis (C(4)A) algorithm. We apply our algorithm to an audiovisual classification task. In comparison to two alternatives, the proposed method demonstrates superior performance.

关键词： semi-supervised learning multi-view learning convex optimization

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learning SMOOTH MODELS OF NONSMOOTH FUNCTIONS VIA CONVEX OPTIMIZATION

LEARNING SMOOTH MODELS OF NONSMOOTH FUNCTIONS VIA CONVEX OPT...

引用

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

作者： Lauer, F. Le, V. L. Bloch, G. Univ Lorraine Inria LORIA CNRSUMR 7503 Paris France Univ Lorraine CRAN CNRS UMR 7039 Paris France

ISBN: (纸本)9781467310260

This paper proposes a learning framework and a set of algorithms for nonsmooth regression, i.e., for learning piecewise smooth target functions with discontinuities in the function itself or the derivatives at unknown locations. In the proposed approach, the model belongs to a class of smooth functions. Though constrained to be globally smooth, the trained model can have very large derivatives at particular locations to approximate the nonsmoothness of the target function. This is obtained through the definition of new regularization terms which penalize the derivatives in a location-dependent manner and training algorithms in the form of convex optimization problems. Examples of application to hybrid dynamical system identification and image reconstruction are provided.

关键词： Location

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