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1st international workshop on Clustering High-Dimensional Data (CHDD)

作者： Bartl, Eduard Belohlavek, Radim Osicka, Petr Rezankova, Hana Palacky Univ Dept Comp Sci Data Anal & Modeling Lab DAMOL CR-77147 Olomouc Czech Republic Univ Econ Fac Informat & Stat Dept Stat & Probabil Prague Czech Republic

ISBN: (纸本)9783662485774;9783662485767

We compare four methods for Boolean matrix factorization (BMF). The oldest of these methods is the 8M method implemented in the BMDP statistical software package developed in the 1960s. The three other methods were developed recently. All the methods compute from an input object-attribute matrix I two matrices, namely an object-factor matrix A and a factor-attribute matrix B in such a way that the Boolean matrix product of A and B is approximately equal to I. Such decompositions are utilized directly in Boolean factor analysis or indirectly as a dimensionality reduction method for Boolean data in machine learning. While some comparison of the BMF methods with matrix decomposition methods designed for real valued data exists in the literature, a mutual comparison of the various BMF methods is a severely neglected topic. In this paper, we compare the four methods on real datasets. In particular, we observe the reconstruction ability of the first few computed factors as well as the number of computed factors necessary to fully reconstruct the input matrix, i.e. the approximation to the Boolean rank of I computed by the methods. In addition, we present some general remarks on all the methods being compared.

关键词： Matrix algebra

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BLIND SEPARATION OF SPATIALLY-BLOCK-SPARSE SOURCES FROM ORTHOGONAL MIXTURES

BLIND SEPARATION OF SPATIALLY-BLOCK-SPARSE SOURCES FROM ORTH...

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23rd IEEE international workshop on machine learning for Signal Processing (MLSP)

作者： Lindenbaum, Ofir Yeredor, Arie Vitek, Ran Mishali, Moshe Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel Altair Hod Hasharon Israel EZ Chip Yokneam Israel

ISBN: (纸本)9781479911806

We addresses the classical problem of blind separation of a static linear mixture, where separation is not based on statistical assumptions (such as independence) regarding the sources, but rather on their spatial (block-) sparsity, and with an additional constraint of an orthogonal mixing-matrix. An algorithm for this problem was recently proposed by Mishali and Eldar, and consists of two steps: one for recovering the support of the sources, and a subsequent one for recovering their values. That algorithm has two shortcomings: One is an assumption that the spatial sparsity level of the sources at each time-instant is constant and known;The second is the algorithm's sensitivity to the possible presence of temporal "blocks" of the signals with identical support. In this work we propose two pre-processing stages for improving the applicability and the performance of the algorithm. A first stage is aimed at identifying "blocks" of similar support, and pruning the data accordingly for the support-recovery stage. A second stage is aimed at recovering the sparsity level at each time-instant by exploiting observed structural inter-relations between the signals at different time-instants. We demonstrate the improvement over the original algorithm using both synthetic data and mixed text-images. We also show that the algorithm outperforms the recovery rate of alternative source separation methods for such contexts, including K-SVD, a leading method for dictionary learning.

关键词： Blind Source Separation Dictionary learning Block-Sparsity Orthogonal Mixtures

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Designing a Multi-Dimensional Space for Hybrid Information Extraction

Designing a Multi-Dimensional Space for Hybrid Information E...

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23rd international workshop on Database and Expert Systems Applications (DEXA)

作者： Feilmayr, Christina Vojinovic, Klaudija Proell, Birgit Johannes Kepler Univ Linz Inst Applicat Oriented Knowledge Proc FAW A-4040 Linz Austria

ISBN: (纸本)9780769548012

Information extraction systems are developed for various specific application domains to manage an increasing amount of unstructured data. The majority build either upon the knowledge-based approach, which promises high accuracy but involves labour-intensive coding of extraction rules, or upon the automatically trainable systems approach, which produces highly portable solutions but requires an appropriate learning set. In this paper, we present results of a project that aims to provide a new methodology which combines the knowledge-based and the machine learning approach into a hybrid one in order to compensate for their respective shortcomings and to achieve high IE performance. firstly, we propose the idea of a multi-dimensional space that guides users in selecting appropriate methods, i.e., different hybrid concepts, depending on the extraction task and the level of available features. Secondly, we provide the concept of one hybrid approach, namely the sequential processing of a knowledge-based approach and a selection of different machine learning methods. Thirdly, we present the evaluation of an implementation of the sequential extraction on a curriculum vitae corpus. Thus, we provide first results for filling the multi-dimensional space for hybrid information extraction.

关键词： Hybrid Information Extraction (statistical) machine learning Extraction Methodology

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Real-time statistical background learning for foreground detection under unstable illuminations

Real-time statistical background learning for foreground det...

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11th IEEE international Conference on machine learning and Applications (ICMLA)

作者： Li, Dawei Xu, Lihong Goodman, Erik Tongji Univ Dept Control Sci & Engn Shanghai Peoples R China Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

ISBN: (纸本)9780769549132;9781467346511

This work proposes a fast background learning algorithm for foreground detection under changing illumination. Gaussian Mixture Model (GMM) is an effective statistical model in background learning. We first focus on Titterington's online EM algorithm that can be used for real-time unsupervised GMM learning, and then advocate a deterministic data assignment strategy to avoid Bayesian computation. The color of the foreground is apt to be influenced by the environmental illumination that usually produce undesirable effect for GMM updating;however, a collinear feature of pixel intensity under changing light is discovered in RGB color space. This feature is afterward used as a reliable clue to decide which part of mixture to update under changing light. A foreground detection step proposed in early version of this work is employed to extract foreground objects by comparing the estimated background model with the current video frame. Experiments have shown the proposed method is able to achieve satisfactory static background images of scenes as well as is also superior to some mainstream methods in detection performance under both indoor and outdoor scenes.

关键词： background learning foreground detection Gaussian mixture model online EM algorithm

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Application of Data Mining to Zheng Studies of Chinese Medicine based on CER

Application of Data Mining to Zheng Studies of Chinese Medic...

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IEEE international Conference on Bioinformatics and Biomedicine workshops (BIBMW)

作者： Cai, Ye-feng Zhang, Yue Liang, Zhao-hui Guangzhou Univ Chinese Med Affiliated Hosp 2 Guangzhou Guangdong Peoples R China Guangzhou Univ Chinese Med Sch Clin Med Guangzhou Peoples R China

ISBN: (纸本)9781467327466;9781467327459

Comparative effectiveness research (CER) is a new clinical study model featured by its strategic framework consists of four categories and three themes. The core strategy of CER is to conduct observational longitude research supported by electronic registry and large database based on real world practice. Since CER studies do not uses a classic randomized control trial (RCT) design, the well-developed data analytic methods for RCTs are challenged. The data groups which are not acquired from the same time point, or have significant difference at the baseline are unable to be compared by the classic differential statistical methods, or the outcome will be without robust statistical support. In this paper, we described the characteristics of the Zheng studies of Chinese medicine. Then some data analytic methods based on machine learning are introduced as potential solutions for the data processing in the CER research of Chinese medicine. Finally, a new strategic framework is introduced to establish the CER methodology for Chinese medicine.

关键词： Comparative effective research machine learning Zheng study Chinese medicine

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A distributed kernel summation framework for general-dimension machine learning

A distributed kernel summation framework for general-dimensi...

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12th SIAM international Conference on Data Mining, SDM 2012

作者： Lee, Dongryeol Vuduc, Richard Gray, Alexander G. Georgia Institute of Technology United States

ISBN: (纸本)9781611972320

Kernel summations are a ubiquitous key computational bottleneck in many data analysis methods. In this paper, we attempt to marry, for the first time, the best relevant techniques in parallel computing, where kernel summations are in low dimensions, with the best general-dimension algorithms from the machine learning literature. We provide the first distributed implementation of kernel summation framework that can utilize: 1) various types of deterministic and probabilistic approximations that may be suitable for low and high-dimensional problems with a large number of data points;2) any multi-dimensional binary tree using both distributed memory and shared memory parallelism;3) a dynamic load balancing scheme to adjust work imbalances during the computation. Our hybrid MPI/OpenMP codebase has wide applicability in providing a general framework to accelerate the computation of many popular machine learning methods. Our experiments show scalability results for kernel density estimation on a synthetic ten-dimensional dataset containing over one billion points and a subset of the Sloan Digital Sky Survey Data up to 6,144 cores. Copyright © 2012 by the Society for Industrial and Applied Mathematics.

关键词： Binary trees

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machine learning for Vision-Based Motion Analysis: Theory and Techniques

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2012年

作者： Liang Wang Guoying Zhao Li Cheng Matti Pietikinen

ISBN: (纸本)9781447126072

Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second international workshop on machine learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, a

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Real-Time statistical Background learning for Foreground Detection under Unstable Illuminations

Real-Time Statistical Background Learning for Foreground Det...

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international Conference on machine learning and Applications (ICMLA)

作者： Dawei Li Lihong Xu Erik Goodman Dept. of Control Science and Engineering Tongji University Shanghai China Dept. of Electrical and Computer Engineering Michigan State University East Lansing USA

This work proposes a fast background learning algorithm for foreground detection under changing illumination. Gaussian Mixture Model (GMM) is an effective statistical model in background learning. We first focus on Titterington's online EM algorithm that can be used for real-time unsupervised GMM learning, and then advocate a deterministic data assignment strategy to avoid Bayesian computation. The color of the foreground is apt to be influenced by the environmental illumination that usually produce undesirable effect for GMM updating, however, a collinear feature of pixel intensity under changing light is discovered in RGB color space. This feature is afterward used as a reliable clue to decide which part of mixture to update under changing light. A foreground detection step proposed in early version of this work is employed to extract foreground objects by comparing the estimated background model with the current video frame. Experiments have shown the proposed method is able to achieve satisfactory static background images of scenes as well as is also superior to some mainstream methods in detection performance under both indoor and outdoor scenes.

关键词： Lighting Image color analysis Vectors Reflection Computational modeling Cameras Indexes

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Stochastic relational processes: Efficient inference and applications

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machine learning 2011年第2期82卷 239-272页

作者： Thon, Ingo Landwehr, Niels De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci B-3001 Heverlee Belgium Univ Potsdam Dept Comp Sci D-14482 Potsdam Germany

One of the goals of artificial intelligence is to develop agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. While standard probabilistic sequence models provide efficient inference and learning techniques for sequential data, they typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient to cope with complex sequential data. In this paper, we introduce a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic (Causal Probabilistic Logic), an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions and employing a Markov assumption, inference and learning become more tractable and effective. Specifically, we show how to solve part of the inference and learning problems directly at the first-order level, while transforming the remaining part into the problem of computing all satisfying assignments for a Boolean formula in a binary decision diagram. We experimentally validate that the resulting technique is able to handle probabilistic relational domains with a substantial number of objects and relations.

关键词： statistical relational learning Stochastic relational process Markov processes Time series CP-Logic

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Impact damage characterisation using a statistical approach

Impact damage characterisation using a statistical approach

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8th international workshop on Structural Health Monitoring 2011: Condition-Based Maintenance and Intelligent Structures

作者： Sultan, M.T.H. Worden, K. Staszewski, W.J. Department of Aerospace Engineering Faculty of Engineering University Putra Malaysia 43400 Serdang Selangor Darul Ehsan Malaysia Department of Mechanical Engineering University of Sheffield Mappin Street Sheffield S1 3JD United Kingdom

ISBN: (纸本)9781605950532

Many of the materials utilised in current technologies, especially those used in the aerospace industry, require an unusual combination of properties that cannot be met by conventional metallic, ceramic or polymeric materials alone. Therefore, aerospace structural engineers are constantly searching for alternative materials, such as composites. Composite materials consist of two or more constituents with varying properties, that when used together, form a new material. These can be tailored to have low densities, high strength and stiffness, and have good corrosion resistance. These materials improve structural efficiency, and can reduce the structural mass without compromising structural stiffness and strength. The ultimate objective of the research work, described in this paper, was to conduct an experimental investigation to detect and quantify impact damage in structures made from composite plates. Two different approaches were used to record acceleration response signals resulting from impact excitation. The first used a standard impulse hammer in order to acquire time data and produce spectra for a number of non-damaging impacts. The second approach used a drop-test to perform a number of potentially damaging impacts. The impact energies for the drop tests ranged from 0.37 J to 41.72 J. Following a systematic series of experiments on the induction of impact damage in composite specimens, two different techniques were used to examine the impacted samples. X-ray radiography was used to evaluate the damage area whilst Scanning Electron Microscopy (SEM) was used to inspect the topographies of the specimens at high magnifications. The motivation behind this research work was based on the idea of using statistical or machine-learning methods to identify and categorise impacts (damaging or non-damaging and also types of failure modes) using only structural response data. The discussion here will be focused on outlier analysis on higher-dimensional multivariate data,

关键词： Aerospace industry

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