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mining social images with distance metric learning for automated image tagging 11

Mining social images with distance metric learning for autom...

引用

Proceedings of the fourth ACM international conference on Web search and data mining

作者： Wu, Pengcheng Hoi, Steven C. H. Zhao, Peilin He, Ying School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

ISBN: (纸本)9781450304931

With the popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machine learning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric learning (UDML) scheme, which not only exploits both visual and textual contents of social images, but also effectively unifies both inductive and transductive metric learning techniques in a systematic learning framework. We further develop an efficient stochastic gradient descent algorithm for solving the UDML optimization task and prove the convergence of the algorithm. By applying the proposed technique to the automated image tagging task in our experiments, we demonstrate that our technique is empirically effective and promising for mining social images towards some real applications. Copyright 2011 ACM.

关键词： Image annotation

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the application of problems concerning the imbalanced data classification by means of support vector machines

The application of problems concerning the imbalanced data c...

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2011 4th international Symposium on Knowledge Acquisition and Modeling, KAM 2011

作者： Qing, Chen Centre of Modern Education Technology Minjiang University Fuzhou China

ISBN: (纸本)9780769545479

Support Vector machine (Support Vector machine, SVM) demonstrates many unique advantages in solving the small sample, nonlinear and high dimensional pattern recognition, and can promote to the application of the use of the function fitting, and other machine learning problems. In order to solve the problem concerning the imbalanced data classification, researchers at home and abroad put forward various solutions based on support vector machines for unbalanced data classification. Many scholars have proposed categories by constructing a small number of samples to compensate for the gap with larger classes to balance the effect, but the new sample is difficult to ensure the same distribution with the original sample, and increases the burden of training devices, or by reducing the number of samples to achieve balance, although this will speed up the training speed, but will reduce the sample information, and the overall error rate of two samples were not reduced. From the perspective of statistical distribution of the sample, the thesis analyzes the reasons resulting in unsatisfactory classification of unbalanced data by support vector machines, and summarizes the uneven performance evaluation index and the progress of unbalanced data classification by support vector machines. © 2011 IEEE.

关键词： Support vector machines

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data mining on Grids

Data Mining on Grids

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4th international conference on Contemporary Computing

作者： Chakraverty, Shampa Gupta, Ankuj Goyal, Akhil Singal, Ashish Netaji Subhas Inst Technol Dept Comp Engn New Delhi 110078 India

ISBN: (纸本)9783642226052

data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. Grid computing emerged as an important new field of distributed computing, which could support the distributed knowledge discovery applications. In this paper, we have proposed a method to perform data mining on Grids. the Grid has been setup using Foster and Kesselman's Globus Toolkit, which is the most widely used middleware in scientific and data intensive grid applications. For the development of data mining applications on grids we have used Weka4WS. Weka4WS is an open source framework extended from the Weka toolkit for distributed data mining on Grid, which deploys many of machine learning algorithms provided by Weka Toolkit. To evaluate the efficiency of the proposed system, a performance analysis of Weka4WS by executing distributed data mining tasks, namely clustering and classification, in grid scenario has been performed. At last, a study on the speed up obtained by doing data mining on grids is done.

关键词： Weka4WS Distributed data mining Grid Computing Classification Clustering Grid

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Evaluation of a Resource Allocating Network with Long Term Memory Using GPU

Evaluation of a Resource Allocating Network with Long Term M...

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10th international conference on Artificial Neural Networks and Genetic Algorithms

作者： Ribeiro, Bernardete Quintas, Ricardo Lopes, Noel Univ Coimbra Dept Informat Engn P-3000 Coimbra Portugal CISUC P-3000 Coimbra Portugal

ISBN: (纸本)9783642202667

Incremental learning has recently received broad attention in many applications of pattern recognition and data mining. With many typical incremental learning situations in the real world where a fast response to changing data is necessary, developing a parallel implementation (in fast processing units) will give great impact to many applications. Current research on incremental learning methods employs a modified version of a resource allocating network (RAN) which is one variation of a radial basis function network (RBFN). this paper evaluates the impact of a Graphics Processing Units (GPU) based implementation of a RAN network incorporating Long Term Memory (LTM) [4]. the incremental learning algorithm is compared with the batch RBF approach in terms of accuracy and computational cost, both in sequential and GPU implementations. the UCI machine learning benchmark datasets and a real world problem of multimedia forgery detection were considered in the experiments. the preliminary evaluation shows that although the creation of the model is faster with the RBF algorithm, the RAN-LTM can be useful in environments with the need of fast changing models and high-dimensional data.

关键词： Incremental learning CPU Computing

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What's your current stress level? Detection of stress patterns from GSR sensor data

What's your current stress level? Detection of stress patter...

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11th IEEE international conference on data mining Workshops, ICDMW 2011

作者： Bakker, Jorn Pechenizkiy, Mykola Sidorova, Natalia Department of Computer Science Eindhoven University of Technology P.O. Box 513 Eindhoven 5600MB Netherlands

ISBN: (纸本)9780769544090

the problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care specialists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. Stress management should start far before the stress start causing illnesses. the current state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. In this paper we (1) formulate the problem of stress identification and categorization from the sensor data stream mining perspective, (2) consider a reductionist approach for arousal identification as a drift detection task, (3) highlight the major problems of dealing with GSR data, collected from a watch-style stress measurement device in normal (i.e. in non-lab) settings, and propose simple approaches how to deal with them, and (4) discuss the lessons learnt from the conducted experimental study on real GSR data collected during the recent field study. © 2011 IEEE.

关键词： pattern recognition

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learning tags from unsegmented videos of multiple human actions

Learning tags from unsegmented videos of multiple human acti...

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11th IEEE international conference on data mining, ICDM 2011

作者： Hospedales, Timothy M. Gong, Shaogang Xiang, Tao Queen Mary University of London E1 4NS United Kingdom

ISBN: (纸本)9780769544083

Providing methods to support semantic interaction with growing volumes of video data is an increasingly important challenge for data mining. To this end, there has been some success in recognition of simple objects and actions in video;however most of this work requires strongly supervised training data. the supervision cost of these approaches therefore renders them economically non-scalable for real world applications. In this paper we address the problem of learning to annotate and retrieve semantic tags of human actions in realistic video data with sparsely provided tags of semantically salient activities. this is challenging because of (1) the multi-label nature of the learning problem and (2) realistic videos are often dominated by (semantically uninteresting) background activity un-supported by any tags of interest, leading to a strong irrelevant data problem. To address these challenges, we introduce a new topic model based approach to video tag annotation. Our model simultaneously learns a low dimensional representation of the video data, which dimensions are semantically relevant (supported by tags), and how to annotate videos with tags. Experimental evaluation on three different video action/activity datasets demonstrate the challenge of this problem, and value of our contribution. © 2011 IEEE.

关键词： data mining

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Ranking web-based partial orders by significance using a Markov reference model

Ranking web-based partial orders by significance using a Mar...

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11th IEEE international conference on data mining, ICDM 2011

作者： Speiser, Michel Antonini, Gianluca Labbi, Abderrahim IBM Zurich Research Laboratory Säumerstrasse 4 CH-8803 Rüschlikon Switzerland

ISBN: (纸本)9780769544083

mining web traffic data has been addressed in literature mostly using sequential pattern mining techniques. Recently, a more powerful pattern called partial order was introduced, with the hope of providing a more compact result set. A further approach towards this goal, valid for both sequential patterns and partial orders, consists in building a statistical significance test for frequent patterns. Our method is based on probabilistic generative models and provides a direct way to rank the extracted patterns. It leaves open the number of patterns of interest, which depends on the application, but provides an alternative criterion to frequency of occurrence: statistical significance. In this paper, we focus on the construction of an algorithm which calculates the probability of partial orders under a first-order Markov reference model, and we show how to use those probabilities to assess the statistical significance of a set of mined partial orders. © 2011 IEEE.

关键词： Probability

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machine learning for Affective Computing 4th

Machine Learning for Affective Computing

引用

4th Bi-Annual international conference of the Humaine Association on Affective Computing and Intelligent Interaction (ACII 2011)

作者： Hoque, Mohammed McDuff, Daniel J. Morency, Louis-Philippe Picard, Rosalind W. MIT Media Lab 77 Massachusetts Ave Cambridge MA 02139 USA Univ Southern California Inst Creat Technol Marina Del Rey CA 90292 USA

ISBN: (纸本)9783642245701

Affective computing (AC) is a unique discipline which includes modeling affect using one or multiple modalities by drawing on techniques from many different fields. AC often deals with problems that are known to be very complex and multi-dimensional, involving different kinds of data (numeric, symbolic, visual etc.). However, with the advancement of machine learning techniques, a lot of those problems are now becoming more tractable.

关键词： spontaneous affective states automated recognition of affect multimodal data fusion probabilistic modeling feature selection

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N DoT: Nearest Neighbor Distance Based Outlier Detection Technique

引用

4th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Hubballi, Neminath Patra, Bidyut Kr. Nandi, Sukumar Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati 781039 Assam India Tezpur Univ Dept Comp Sci & Engn Tezpur India

ISBN: (纸本)9783642217869

In this paper, we propose a nearest neighbor based outlier detection algorithm, N DoT. We introduce a parameter termed as Nearest Neighbor Factor (NNF) to measure the degree of outlierness of a point with respect to its neighborhood. Unlike the previous outlier detection methods N DoT works by a voting mechanism. Voting mechanism binarizes the decision compared to the top-N style of algorithms. We evaluate our method experimentally and compare results of N DoT with a classical outlier detection method LOF and a recently proposed method LDOF. Experimental results demonstrate that N DoT outperforms LDOF and is comparable with LOF.

关键词： data handling

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Improving models of slipping, guessing, and moment-by-moment learning with estimates of skill difficulty

Improving models of slipping, guessing, and moment-by-moment...

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4th international conference on Educational data mining, EDM 2011

作者： Gowda, Sujith M. Rowe, Jonathan P. Baker, Ryan S.J.D. Chi, Min Koedinger, Kenneth R. Department of Social Science and Policy Studies Worcester Polytechnic Institute Worcester MA United States Department of Computer Science North Carolina State University 890 Oval Drive Raleigh United States Developmental and Psychological Sciences Stanford University 485 Lasuen Mall Stanford CA 94305 United States Department of Social Science and Policy Studies Human-Computer Interaction Institute Carnegie Mellon University 5000 Forbes Ave. Pittsburgh PA 15213 United States

ISBN: (纸本)9789038625379

Over the past several years, several extensions to Bayesian knowledge tracing have been proposed in order to improve predictions of students' in-tutor and post-test performance. One such extension is Contextual Guess and Slip, which incorporates machine-learned models of students' guess and slip behaviors in order to enhance the overall model's predictive performance [Baker et al. 2008a]. Similar machine learning approaches have been introduced in order to detect specific problem-solving steps during which students most likely learned particular skills [Baker, Goldstein, and Heffernan in press]. However, one important class of features that have not been considered in machine learning models used in these two techniques is metrics of item and skill difficulty, a key type of feature in other assessment frameworks [e.g Hambleton, Swaminathan, & Rogers, 1991;Pavlik, Cen, & Koedinger 2009]. In this paper, a set of engineered features that quantify skill difficulty and related skill-level constructs are investigated in terms of their ability to improve models of guessing, slipping, and detecting moment-by-moment learning. Supervised machine learning models that have been trained using the new skill-difficulty features are compared to models from the original contextual guess and slip and moment-by-moment learning detector work. this includes performance comparisons for predicting students' in-tutor responses, as well as post-test responses, for a pair of Cognitive Tutor data sets.

关键词： Students

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