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检索条件"任意字段=International Conference on Signal and Data Processing, ICSDP 2019"

共 2693 条记录，以下是41-50 订阅

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EVOLUTIONARY SUBSPACE CLUSTERING: DISCOVERING STRUCTURE IN SELF-EXPRESSIVE TIME-SERIES data 44

EVOLUTIONARY SUBSPACE CLUSTERING: DISCOVERING STRUCTURE IN S...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Hashemi, Abolfazl Vikalo, Haris Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

ISBN: (纸本)9781479981311

An evolutionary self-expressive model for clustering a collection of evolving data points that lie on a union of low-dimensional evolving subspaces is proposed. A parsimonious representation of data points at each time step is learned via a non-convex optimization framework that exploits the self-expressiveness property of the evolving data while taking into account data representation from the preceding time step. The resulting scheme adaptively learns an innovation matrix that captures changes in self-representation of data in consecutive time steps as well as a smoothing parameter reflective of the rate of data evolution. Extensive experiments demonstrate superiority of the proposed framework overs state-of-the-art static subspace clustering algorithms and existing evolutionary clustering schemes.

关键词： subspace clustering evolutionary clustering self-expressive models temporal data real-time clustering

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A survey of signal processing based graph learning techniques 1

A survey of signal processing based graph learning technique...

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1st international conference on Industrial Artificial Intelligence (IAI)

作者： Subbareddy, B. Reddy, P. Charantej Siripuram, Aditya Zhang, Jingxin Indian Inst Technol Hyderabad Hyderabad India Swinburne Univ Technol Melbourne Vic Australia

ISBN: (纸本)9781728135939

Recent advances in technology have led to easy data acquisition mechanisms in many fields, leading to massive datasets. It is often of interest to understand the inherent structure and learn the best representation of the given dataset. Graphs are a powerful way to model interrelationships between data features - well constructed meaningful graphs help in representing and processing the data effectively. The graph topology needs to be inferred from the observed data. In this survey, we briefly explore signal processing based graph learning approaches that have been proposed in the literature and propose new research directions.

关键词： Graph signal processing graph learning network topology inference sparse signal processing

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ESTIMATING THE NUMBER OF CORRELATED COMPONENTS BASED ON RANDOM PROJECTIONS 44

ESTIMATING THE NUMBER OF CORRELATED COMPONENTS BASED ON RAND...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Lameiro, Christian Hasija, Tanuj Marrinan, Tim Schreier, Peter J. Uniyersitat Paderborn Signal & Syst Theory Grp Paderborn Germany

ISBN: (纸本)9781479981311

Estimating the number of correlated components between two data sets is a challenging task in the case of small sample support. Typically, a rank-reduction preprocessing step based on principal component analysis (PCA) is carried out on each data set individually to reduce the dimensionality before analyzing correlation between the data sets. However, PCA retains the components with the largest variance within a data set, and therefore fails when these components are not the ones that account for the correlation between the data sets. To overcome this, we propose an alternative technique that, instead of projecting the data into a single subspace, uses a large number of random projections.

关键词： Correlation analysis small sample support random projection KL divergence

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A LEARNING-BASED FRAMEWORK FOR LINE-SPECTRA SUPER-RESOLUTION 44

A LEARNING-BASED FRAMEWORK FOR LINE-SPECTRA SUPER-RESOLUTION

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Izacard, Gautier Bernstein, Brett Fernandez-Granda, Carlos NYU Ctr Data Sci New York NY 10003 USA Ecole Polytech Paris France NYU Courant Inst Math Sci New York NY USA

ISBN: (纸本)9781479981311

We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra of such signals on simulated data. The proposed methodology is very flexible: adapting to different signal and noise models only requires modifying the training data accordingly. Numerical experiments show that the approach performs competitively with classical methods designed for additive Gaussian noise at a range of noise levels, and is also effective in the presence of impulsive noise.

关键词： Line-spectra estimation spectral super-resolution deep learning neural networks sparse noise

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SAFETY IN THE FACE OF UNKNOWN UNKNOWNS: ALGORITHM FUSION IN data-DRIVEN ENGINEERING SYSTEMS 44

SAFETY IN THE FACE OF UNKNOWN UNKNOWNS: ALGORITHM FUSION IN ...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Kshetry, Nina Varshney, Lav R. Ensaras Inc Champaign IL 61821 USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781479981311

Most current machine learning algorithms make highly confident yet incorrect classifications when faced with unexpected test samples from an unknown distribution different from training;such epistemic uncertainty (unknown unknowns) can have catastrophic safety implications. In this conceptual paper, we propose a method to leverage engineering science knowledge to control epistemic uncertainty and maintain decision safety. The basic idea is an algorithm fusion approach that combines data-driven learned models with physical system knowledge, to operate between the extremes of purely data-driven classifiers and purely engineering science rules. This facilitates the safe operation of data-driven engineering systems, such as wastewater treatment plants.

关键词： AI safety algorithm fusion epistemic uncertainty metacognition wastewater treatment

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RELATIONSHIPS BETWEEN DEEP LEARNING AND LINEAR ADAPTIVE SYSTEMS 44

RELATIONSHIPS BETWEEN DEEP LEARNING AND LINEAR ADAPTIVE SYST...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Douglas, Scott C. Larson, Eric C. Southern Methodist Univ Dept Elect & Comp Engn Dallas TX 75275 USA Southern Methodist Univ Dept Comp Sci Dallas TX 75275 USA

ISBN: (纸本)9781479981311

Linear adaptive systems are a well-known staple in numerous signal processing applications. Recently, significant activity and performance gains have been achieved in multilayer neural networks for deep learning applied to practical data processing applications. In this paper, we describe the important relationships and significant differences between the procedures and methods used in linear adaptive systems and those used in multilayer neural networks for deep learning tasks. Input-output structures, cost functions and training criteria, adaptive algorithms, and data processing and optimization strategies are considered. It is the hope of the authors that this discussion will spur further crossover between the two fields, and in particular allow knowledge to be shared and further progress to be made.

关键词： adaptive signal processing adaptive systems deep learning machine learning neural networks

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Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing

Recurrent Neural Network-Assisted Adaptive Sampling for Appr...

引用

IEEE international conference on Big data (Big data)

作者： Feng, Yi Zhou, Yi Tarokh, Vahid Duke Univ Elect & Comp Engn Durham NC 27706 USA Univ Utah Elect & Comp Engn Salt Lake City UT USA

ISBN: (纸本)9781728108582

We propose an adaptive signal sampling approach that dynamically adjusts the sampling rate to approximate the local Nyquist rate of the signal. The proposed adaptive sampling approach consists of a recurrent neural network-based change detector that detects the point of frequency change and a local Nyquist rate estimator based on a multi-rate signal processing scheme. We empirically demonstrate that our adaptive sampling approach significantly reduces the overall sampling rate for various types of signals and therefore improves the computational efficiency of subsequent signal processing.

关键词： Adaptive sampling approximate computing neural networks machine learning signal processing

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Energy-Efficient Channel Access Considering data Priority in Cognitive Radio Sensor Networks 11

Energy-Efficient Channel Access Considering Data Priority in...

引用

11th IEEE international conference on Wireless Communications and signal processing (WCSP)

作者： Yang, Hanmeng Chen, Hongbin Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China

ISBN: (纸本)9781728135557

Wireless sensor network (WSN), as a key provider of big data, will generate a large amount of data, which requires sufficient spectrum and energy resources to transmit. Cognitive radio sensor networks (CRSNs) solve the problem of increasing shortage of spectrum resources in big data environment by using dynamic spectrum access technology, while energy consumption is still the main task of current research. Therefore, in this paper, considering the characteristics of big data, that is, the difference in the amount of data to be transmitted by each node, we propose a dynamic channel access scheme based on data priority and energy consumption minimization, which first allocates transmission power according to the data priority of each node, and then allocates transmission time reasonably to each node aiming at minimizing energy consumption. Finally, the effectiveness of the proposed scheme is proved by simulation results.

关键词： Cognitive radio sensor networks data transmission dynamic channel access data priority energy consumption minimization

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THE DISCRETE COSINE TRANSFORM ON TRIANGLES 44

THE DISCRETE COSINE TRANSFORM ON TRIANGLES

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44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Seifert, Bastian Hueper, Knut Ansbach Univ Appl Sci Fac Engn Sci Ansbach Germany Univ Wurzburg Inst Math Wurzburg Germany

ISBN: (纸本)9781479981311

The discrete cosine transform is a valuable tool in analysis of data on undirected rectangular grids, like images. In this paper it is shown how one can define an analogue of the discrete cosine transform on triangles. This is done by combining algebraic signal processing theory with a specific kind of multivariate Chebyshev polynomials. Using a multivariate Christoffel-Darboux formula it is shown how to derive an orthogonal version of the transform.

关键词： discret cosine transform algebraic signal processing Christoffel-Darboux formula multivariate Chebyshev polynomials lattice of triangles

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FROM LOCAL TO GLOBAL SUBSPACE CLUSTERING FOR IMAGE data 44

FROM LOCAL TO GLOBAL SUBSPACE CLUSTERING FOR IMAGE DATA

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44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Abdolali, Maryam Rahmati, Mohammad Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran

ISBN: (纸本)9781479981311

The subspace clustering problem arises in many applications that involve processing high-dimensional data, i.e. images and videos. In many of these applications, high dimensional data is often well approximated by union of low-dimensional subspaces. This motivated the development of various algorithms to cluster high dimensional data based on the underlying intrinsic low-dimensional subspaces. However, the existing approaches are based on global representation of data whereas this representation can be easily affected by errors, occlusions and severe illumination conditions. Here, we propose a multi-scale approach based on extracting local patches from different scales and then merging the shared information using a weighted scheme based on Grassmann manifolds. This approach not only benefits from the discriminative information from global representation of data but also makes the clustering task more robust using the information from local representations. Numerical results show that the proposed approach significantly outperforms existing subspace clustering algorithms.

关键词： Subspace clustering Grassmann manifold Multi-Scale approximation Multilayer graph

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