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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21-30 订阅

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HETEROSCEDAstIC GAUSSIAN PROCESS REGRESSION USING EXPECTATION PROPAGATION

HETEROSCEDASTIC GAUSSIAN PROCESS REGRESSION USING EXPECTATIO...

引用

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

作者： Munoz-Gonzalez, Luis Lazaro-Gredilla, Miguel Figueiras-Vidal, Anibal R. Univ Carlos III Madrid Signal Theory & Commun Dept E-28903 Getafe Spain Univ Cantabria Dept Commun Engn Santander Spain

ISBN: (纸本)9781457716232

Gaussian Processes (GPs) are Bayesian non-parametric models that achieve state-of-the-art performance in regression tasks. To allow for analytical tractability, noise power is usually considered constant in these models, which is unrealistic for many real world problems. In this work we consider a GP model with heteroscedastic (i.e., input dependent) noise power, and then, use Expectation Propagation (EP) to perform approximate inference on it. The proposed EP approach is much faster than Markov Chain Monte Carlo and more accurate than competing methods of similar computational cost. This superiority is illustrated in several experiments with synthetic and real-world data.

关键词： noise power Markov chain monte carlo methods Gaussian processes World problems ESSENTIAL POWER Regression (Psychology) European Parliament Gauss SYNTHETICS

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mlsp2011 COMPETITION: FACE RECOGNITION WITH INTEGRATING MULTIPLE CUES

MLSP2011 COMPETITION: FACE RECOGNITION WITH INTEGRATING MULT...

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

作者： Han, Zhao-Cui Tang, Xu-Sheng Li, Yun-Feng Wang, Guo-Qiang Su, Tie-Ming Ou, Fan Ou, Zong-Ying Xu, Wen-Ji Dalian Univ Technol Dalian Peoples R China Fuzhou Univ Fuzhou Peoples R China Henan Univ Sci & Technol luoyang Peoples R China Luoyang Inst Sci & Technol Luoyang Peoples R China

ISBN: (纸本)9781457716232

Automatic face recognition is a challenge task, especially working in practical uncontrolled environment. Over the past two decades, numerous innovative ideas and effective processing approaches have been proposed and developed, e. g. different normalization techniques, intrinsic feature extractions and representation schemes, machine learning methods and recognition mechanisms etc. processing with integrating multiple cues is an effective approach for upgrading recognition performance. A multiple stage with integrating Harr, gradient and cuvelete features for locating eye center approach is presented. The integration based approach achieved high location accuracy and outperforms other state of the art methods. A face recognition classifier using integrating Gabor feature representation and curvelet feature representation is also presented. The experiment results show that the fusion processing can reduce error rate about 30%, compared with using Gabor feature or curvelet feature alone.

关键词： Face recognition multi cues locating eye center curvelet

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stOCHAstIC KERNEL TEMPORAL DIFFERENCE FOR REINFORCEMENT learning

STOCHASTIC KERNEL TEMPORAL DIFFERENCE FOR REINFORCEMENT LEAR...

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

作者： Bae, Jihye Giraldo, Luis Sanchez Chhatbar, Pratik Francis, Joseph Sanchez, Justin Principe, Jose Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Suny Downstate Med Ctr Dept Physiol Brooklyn NY 11203 USA Miami Univ Dept Biomed Engn Oxford OH 45056 USA

ISBN: (纸本)9781457716232

This paper introduces a kernel adaptive filter using the stochastic gradient on temporal differences, kernel TD(lambda), to estimate the state-action value function Q in reinforcement learning. Kernel methods are powerful for solving nonlinear problems, but the growing computational complexity and memory size limit their applicability on practical scenarios. To overcome this, the quantization approach introduced in [1] is applied. To help understand the behavior and illustrate the role of the parameters, we apply the algorithm on a 2-dimentional spatial navigation task. Eligibility traces are commonly applied in TD learning to improve data efficiency, so the relations of eligibility trace. and step size and filter size are observed. Moreover, kernel TD (0) is applied to neural decoding of an 8 target center-out reaching task performed by a monkey. Results show the method can effectively learn the brain-state action mapping for this task.

关键词： Temporal difference learning kernel methods reinforcement learning adaptive filtering

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SPEAKER SEGMENTATION AND CLUstERING BASED ON THE IMPROVED SPECTRAL CLUstERING

SPEAKER SEGMENTATION AND CLUSTERING BASED ON THE IMPROVED SP...

引用

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

作者： Ma, Yong Bao, Chang-chun Liu, Jia Beijing Univ Technol Sch Elect Informat & Control Engn Speech & Audio Signal Proc Lab Beijing 100124 Peoples R China Tsinghua Univ Dept Elect Engn Natl Tsing Lab Informat Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9781457716232

Efficient speaker segmentation and clustering method based on the improved spectral clustering is proposed in this paper. Traditional speaker segmentation and clustering is performed by the hierarchical clustering algorithms with Bayesian information criterion (BIC) metric and cross likelihood ratio (CLR) metric after the speakers are segmented. Since this method has high computational complexity and may result in a suboptimal solution, we use spectral clustering to overcome this problem and improve the performance of clustering algorithm. First the affinity matrix is constructed with the mean supervector feature transformed by KL kernel mapping. And then the scaling parameter is selected adaptively. The experiments performed on the NIst 1998 multi-speaker corpus show that the proposed method outperforms the baseline system.

关键词： Speaker segmentation and clustering Bayesian information criterion Spectral Clustering

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A CLOUD COMPUTING SERVICE FOR FAst AUDIO SOURCE signal SEPARATION

A CLOUD COMPUTING SERVICE FOR FAST AUDIO SOURCE SIGNAL SEPAR...

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

作者： Liang, Tyng-Yeu Wang, Ti-Hsin Chou, Meng-Te Chen, Shiou-Wen Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan

ISBN: (纸本)9781457716232

In this study, we propose a cloud computing service for fast audio source signal separation. To implement this service, we have developed a GPU-based ICA (Independent Component Analysis) program by using CUDA software development toolkit (SDK). This program can rapidly separate n independent components from m sources which are composed of these components mixed by a random way. On the other hand, we also have implemented a web server to provide users with the service of fast audio signal separation. Users can upload their audio files onto this web server through the browser interface. After users click the execution command button in the web page, the web server will invoke the GPU-based ICA program to fast separate the independent audio signals from the uploaded files. Finally, users can download and play the wave files of the separated audio signals from the web server by clicking the hyperlink in the website.

关键词： Cloud computing audio source separation GPU CUDA independent component analysis

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SEMI-SUPERVISED HIERARCHY learning USING MULTIPLE-LABELED DATA

SEMI-SUPERVISED HIERARCHY LEARNING USING MULTIPLE-LABELED DA...

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

作者： Javadi, Ailar Gray, Alexander Anderson, David Berisha, Visar Georgia Inst Technol Atlanta GA 30332 USA Arizona State Univ Phoenix AZ 85287 USA

ISBN: (纸本)9781457716232

While hierarchical semi-supervised classification methods have been previously studied, we still lack an algorithm that can learn a non-predefined categorical hierarchy from multi-labeled data at various levels of specificity. Inspired by human psychology and learning experience, in this paper we propose a semi-supervised learning method that can classify multi-labeled data into a hierarchy based on the label's specificity level such that the separability between each class and its siblings is greater than the separability between each class and its parents. To build the hierarchy we show that a minimum spanning tree minimizes an upper bound on the pairwise Kullback-Liebler divergence between the true and approximated distributions. We show the effectiveness of our method using three types of data sets and draw a comparison between our learned hierarchy and one learned by human subjects using the same data set. We also show the effectiveness of our method compared to hierarchical clustering.

关键词： Supervised learning

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BAYESIAN NONPARAMETRIC MODELING OF HIERARCHICAL TOPICS AND SENTENCES

BAYESIAN NONPARAMETRIC MODELING OF HIERARCHICAL TOPICS AND S...

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

作者： Chang, Ying-Lan Hung, Jui-Jung Chien, Jen-Tzung Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781457716232

Automatically scoring the sentences of multiple documents plays an important role for document summarization. This study presents a new Bayesian nonparametric approach to conduct unsupervised learning of a hierarchical topic and sentence model (HTSM). This HTSM discovers an extended hierarchy in the nested Chinese restaurant process (nCRP) where each sentence is assigned by a hierarchical topic path. A tree structure with distributions ranging from broad topics to precise topics is established. The dependencies among sentences are characterized. The words in different sentences are represented by a shared hierarchical Dirichlet process (HDP). The topic mixtures in word level and sentence level are estimated according to unsupervised nonparametric processes based on HDP and nCRP, respectively. Compared with the nCRP representing a document based on a single path, the proposed HTSM is flexible with a new nCRP where multiple paths are incorporated to generate different sentences of a document. A summarization system is developed to extract semantically-rich sentences from documents. A new Gibbs sampling algorithm is developed to infer the structural parameters of HTSM. In the experiments on DUC corpus, the proposed HTSM outperforms the other methods for document summarization in terms of ROUGE measures.

关键词： Topic model unsupervised learning Bayesian nonparametrics document summarization

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A BLOCK-BASED APPROACH TO ADAPTIVELY BIAS THE WEIGHTS OF ADAPTIVE FILTERS

A BLOCK-BASED APPROACH TO ADAPTIVELY BIAS THE WEIGHTS OF ADA...

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

作者： Azpicueta-Ruiz, Luis A. Lazaro-Gredilla, Miguel Figueiras-Vidal, Anibal R. Arenas-Garcia, Jeronimo Univ Carlos III Madrid Dept Signal Theory & Commun E-28903 Getafe Spain Univ Cantabria Dept Commun Engn Cantabria Spain

ISBN: (纸本)9781457716232

Adaptive filters are crucial in many signal processing applications. Recently, a simple configuration was presented to introduce a bias in the estimation of adaptive filters using a multiplicative factor, showing important gains in terms of mean square error with respect to standard adaptive filter operation, mainly for low signal to noise ratios. In this paper, we modify that scheme to obtain further advantages by splitting the adaptive filter coefficients into non-overlapping blocks, and employing a different multiplicative factor for the coefficients in each block. In this way, bias vs variance compromise is managed independently in each block, allowing an enhancement if the energy of the unknown system is non-uniformly distributed. In order to give some insight on the behavior of the scheme, a theoretical analysis of the optimal scaling factors is developed. In addition, several sets of experiments are included to widely study the new scheme performance.

关键词： Adaptive filters biased estimation combination of filters sparse system identification

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MULTI-PARAMETRIC SOLUTION-PATH ALGORITHM FOR INstANCE-WEIGHTED SUPPORT VECTOR machineS

MULTI-PARAMETRIC SOLUTION-PATH ALGORITHM FOR INSTANCE-WEIGHT...

引用

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

作者： Karasuyama, Masayuki Harada, Naoyuki Sugiyama, Masashi Takeuchi, Ichiro Tokyo Inst Technol Dept Comp Sci Tokyo 1528552 Japan Nagoya Inst Technol Dept Engn Nagoya Aichi 466 Japan

ISBN: (纸本)9781457716232

An instance-weighted variant of the support vector machine (SVM) has attracted considerable attention recently since they are useful in various machine learning tasks such as non-stationary data analysis, heteroscedastic data modeling, transfer learning, learning to rank, and transduction. An important challenge in these scenarios is to overcome the computational bottleneck-instance weights often change dynamically or adaptively, and thus the weighted SVM solutions must be repeatedly computed. In this paper, we develop an algorithm that can efficiently and exactly up-date the weighted SVM solutions for arbitrary change of instance weights. Technically, this contribution can be regarded as an extension of the conventional solution-path algorithm for a single regularization parameter to multiple instance-weight parameters. However, this extension gives rise to a significant problem that breakpoints (at which the solution path turns) have to be identified in high-dimensional space. To facilitate this,we introduce a parametric representation of instance weights which allows us to find the breakpoints in high-dimensional space easily. Despite its simplicity, our parametrization covers various important machine learning tasks and it widens the applicability of the solution-path algorithm. Through extensive experiments on various practical applications, we demonstrate the usefulness of the proposed algorithm

关键词： Support Vector Network Breakpoints parametric representation algorithms Artificial Intelligence machine learning parameterization

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PUTTING POSES ON MANIFOLD FOR ACTION RECOGNITION

PUTTING POSES ON MANIFOLD FOR ACTION RECOGNITION

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

作者： Cao, Xianbin Ning, Bo Yan, Pingkun Li, Xuelong Univ Sci & Technol China Dept Comp Sci & Technol Hefei 230026 Peoples R China Beihang Univ Beijing 100083 Peoples R China Chinese Acad Sci Ctr Opt Imagery Anal & Learning OPTIMAL Xian Inst Opt & Precis Mech State Key Lab Transient Opt & Photon Xian 710119 Peoples R China

ISBN: (纸本)9781457716232

In action recognition, bag of words based approaches have been shown to be successful, for which the quality of codebook is critical. This paper proposes a novel approach to select key poses for the codebook, which models the descriptor space utilizing manifold learning to recover the geometric structure of the descriptors on a lower dimensional manifold space. A PageRank based centrality measure is developed to select key poses on the manifold. In each step, a key pose is selected and the remaining model is modified to maximize the discriminative power of selected codebook. In classification, the ambiguity of each action couple is evaluated through cross validation. An additional subdivision will be executed for ambiguous pairs. Experiments on ut-tower dataset showed that our method is able to obtain better performance than the state-of-the-art methods.

关键词： Action Recognition Key poses Bag of Words Manifold Leaning Centrality Measure

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