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3rd international conference on Consumer Electronics, Communications and Networks (CECNet)

作者： He, Ling-Min Jin, Wei Yang, Xiao-Bin Wang, Kang-Jian China Jiliang Univ Coll Informat Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781479928606

The Locally Linear Embedding (LLE) is one of the efficient nonlinear dimensionality reduction techniques. But for some high dimensional data, it is not taking the class information of the data into account and Euclidean distance can not accurately reflect the similarity among samples. The paper proposes an improved Supervised LLE which combines class labeled data and Mahalanobis Distance (MSP-LLE). First, the approach learns a Mahalanobis Distance from the existing data. Then the Mahalanobis Distance and label information are combined to choose neighborhoods. Finally, ELM is using to map the unlabeled data to the feature space, which easily implement fault pattern recognition. The experiment result shows its good performance on reduction and recognition for high-dimensional and similar data.

关键词： locally linear embedding extreme learning machine Mahalanobis distance supervised reduction recognition

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machine learning for activity recognition: hip versus wrist data

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PHYSIOLOGICAL MEASUREMENT 2014年第11期35卷 2183-2189页

作者： Trost, Stewart G. Zheng, Yonglei Wong, Weng-Keen Queensland Univ Technol Sch Exercise & Nutr Sci Inst Hlth & Biomed Innovat Kelvin Grove Qld 4059 Australia Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

Problem addressed: Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology: 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results: Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%);acceptable classification accuracy for lying down (88.3%) and basketball (81.9%);and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%);acceptable classification accuracy for basketball (86.0%);and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact: Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

关键词： accelerometer objective monitoring children

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Localizing and comparing weight maps generated from linear kernel machine learning models

Localizing and comparing weight maps generated from linear k...

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3rd international Workshop on pattern recognition in NeuroImaging (PRNI)

作者： Schrouff, J. Cremers, J. Garraux, G. Baldassarre, L. Mourao-Miranda, J. Phillips, C. Univ Liege Cyclotron Res Ctr B-4000 Liege Belgium Ecole Polytech Fed Lausanne Lausanne Switzerland UCL Dept Comp Sci London England

ISBN: (纸本)9780769550619

Recently, machine learning models have been applied to neuroimaging data, allowing to make predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. These pattern recognition based methods present undeniable assets over classical (univariate) techniques, by providing predictions for unseen data, as well as the weights of each voxel in the model. However, the obtained weight map cannot be thresholded to perform regionally specific inference, leading to a difficult localization of the variable of interest. In this work, we provide local averages of the weights according to regions defined by anatomical or functional atlases (e. g. Brodmann atlas). These averages can then be ranked, thereby providing a sorted list of regions that can be (to a certain extent) compared with univariate results. Furthermore, we defined a "ranking distance", allowing for the quantitative comparison between localized patterns. These concepts are illustrated with two datasets.

关键词： machine learning fMRI pattern localization ranking pattern comparison

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The 3rd international Workshop on Intelligent data Analysis and Management 1

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丛书名： Springer Proceedings in Complexity

2013年

作者： Galit Shmueli, Ron Kenett (auth.), Lorna Uden, Leon S.L. Wang, Tzung-Pei Hong, Hsin-Chang Yang, I-Hsien Ting (eds.)

ISBN: (数字)9789400772939

ISBN: (纸本)9789400772922;9789400772939

These papers on Intelligent data Analysis and Management (IDAM) examine issues related to the research and applications of Artificial Intelligence techniques in data analysis and management across a variety of disciplines. The papers derive from the 2013 IDAM conference in Kaohsiung ,Taiwan. It is an interdisciplinary research field involving academic researchers in information technologies, computer science, public policy, bioinformatics, medical informatics, and social and behavior studies, etc. The techniques studied include (but are not limited to): data visualization, data pre-processing, data engineering, database mining techniques, tools and applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing, etc.

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Automatic differentiation between Alzheimer's Disease and Mild Cognitive Impairment combining PET data and psychological scores

Automatic differentiation between Alzheimer's Disease and Mi...

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3rd international Workshop on pattern recognition in NeuroImaging (PRNI)

作者： Segovia, F. Bastin, C. Salmon, E. Gorriz, J. M. Ramirez, J. Phillips, C. Univ Liege Cyclotron Res Ctr B-4000 Liege Belgium Univ Granada Network & Commun Dept Signal Theory E-18071 Granada Spain Univ Liege Dept Elect Engn & Comp Sci B-4000 Liege Belgium

ISBN: (纸本)9780769550619

In recent years, several approaches to develop computer aided diagnosis systems for dementia have been proposed. The purpose of this work is to measure the advantages of using not only brain images as data source for those systems but also some psychological scores. To this aim, we compared the accuracy rates achieved by systems that use psychological scores beside the image data in the classification step and systems that use only the image data. The experiments show that the formers achieve higher accuracy rates regardless of the procedure carried out to analyze the image data.

关键词： Alzheimer's disease mild cognitive impairment machine learning PET imaging

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Handwritten Character recognition Using Histograms of Oriented Gradient Features in Deep learning of Artificial Neural Network

Handwritten Character Recognition Using Histograms of Orient...

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3rd international conference on IT Convergence and Security (ICITCS)

作者： Iamsa-at, Suthasinee Horata, Punyaphol Khon Kaen Univ Fac Sci Dept Comp Sci Khon Kaen Thailand

ISBN: (纸本)9781479928453

Feature extraction plays an essential role in hand written character recognition because of its effect on the capability of classifiers. This paper presents a framework for investigating and comparing the recognition ability of two classifiers: Deep-learning Feedforward-Backpropagation Neural Network (DFBNN) and Extreme learning machine (ELM). Three data sets: Thai handwritten characters, Bangla handwritten numerals, and Devanagari handwritten numerals were studied. Each data set was divided into two categories: non-extracted and extracted features by Histograms of Oriented Gradients (HOG). The experimental results showed that using HOG to extract features can improve recognition rates of both of DFBNN and ELM. Furthermore, DFBNN provides higher slightly recognition rates than those of ELM.

关键词： Handwritten Character recognition Feature extraction Histograms of Oriented Gradients Deep learning Neural Network Extreme learning machine

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Sparse network-based models for patient classification using fMRI

Sparse network-based models for patient classification using...

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3rd international Workshop on pattern recognition in NeuroImaging (PRNI)

作者： Rosa, Maria J. Portugal, Liana Shawe-Taylor, John Mourao-Miranda, Janaina UCL Dept Comp Sci Ctr Computat Stat & Machine Learning London WC1E 6BT England

ISBN: (纸本)9780769550619

pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has been successful at discriminating psychiatric patients from healthy subjects. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. As is generally accepted, many psychiatric disorders, such as depression and schizophrenia, are brain connectivity disorders. Therefore, pattern recognition based on network models should provide more scientific insight and potentially more powerful predictions than voxel-based approaches. Here, we build a sparse network-based discriminative modelling framework, based on Gaussian graphical models and L1-norm regularised linear Support Vector machines (SVM). The proposed framework provides easier pattern interpretation in terms of underlying network changes between groups, and we illustrate our technique by classifying patients with depression and controls, using fMRI data from a sad facial processing task.

关键词： sparse models graphical LASSO L1-norm SVM functional brain connectivity major depression disorder fMRI

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Local-Aggregate Modeling for Multi-Subject Neuroimage data via Distributed Optimization

Local-Aggregate Modeling for Multi-Subject Neuroimage Data v...

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3rd international Workshop on pattern recognition in NeuroImaging (PRNI)

作者： Hu, Yue Allen, Genevera I. Rice Univ Dept Stat Houston TX 77251 USA Rice Univ Jan & Dan Duncan Neurol Res Inst Dept Stat & Elect & Comp Engn Houston TX 77251 USA

ISBN: (纸本)9780769550619

Developing multi-subject predictive models based on whole-brain neuroimage data for each subject is a major challenge due to the spatio-temporal nature of the variables and the massive amount of data relative to the number of subjects. We propose a novel multivariate machine learning model and algorithmic strategy for multi-subject regression or classification that uses regularization to directly account for the spatio-temporal nature of the data. Our method begins by fitting multi-subject models to each location separately (similar to univariate frameworks), and then aggregates information across nearby locations through regularization. We develop an optimization strategy so that our so called, Local-Aggregate Models, can be fit in a completely distributed manner over the locations which greatly reduces computational costs. Our models achieve better predictions with more interpretable results as demonstrated through a multi-subject EEG example.

关键词： multi-subject data neuroimaging parallel computing EEG generalized linear models

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Wrapper methods to correct mislabelled training data

Wrapper methods to correct mislabelled training data

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3rd international Workshop on pattern recognition in NeuroImaging (PRNI)

作者： Young, Jonathan Ashburner, John Ourselin, Sebastien UCL Ctr Med Image Comp London England UCL Inst Neurol Funct Imaging Lab London England

ISBN: (纸本)9780769550619

machine learning has obvious applications to the diagnosis of disease, and for many neurological conditions features extracted from brain images allow classifiers based on neuroimaging biomarkers to provide a useful complement to more traditional diagnostic methods based on symptoms and psychological testing. However the labels used in the training of such systems frequently depend on standard clinical diagnostic methods, meaning they are not completely reliable in many cases. This uncertainty makes the problems this causes hard to study, as it is difficult to measure both the extent of mislabelling and its effect on results. To avoid this problem, we perform classification of gender based on imaging, as this is definitely known for each subject. We then deliberately make known proportions of the training labels incorrect. This allows us to assess the effect of the level of label noise on classification accuracy, and evaluate methods that allow for the mislabelled data. The methods are wrappers using existing well known classifier algorithms. The results indicate that the methods can be significantly effective at realistic levels of noise in the training labels, but care must be taken in choosing which method to apply depending on the level of label noise.

关键词： classification misdiagnosis diagnosis SVM Gaussian processes MRI noise level training data correction

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Persian Handwritten Digits recognition by Using Zoning and Histogram Projection

Persian Handwritten Digits Recognition by Using Zoning and H...

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3rd Joint conference of AI and Robotics / 5th RoboCup Iran Open international Symposium

作者： Nooraliei, Amir Islamic Azad Univ Qazvin Branch Dept Elect Comp & IT Engn Qazvin Iran

ISBN: (纸本)9781467363150

In this paper, Persian handwritten digits reorganization by using zoning features and projection histogram for extracting feature vectors with 69-dimensions is presented. In classification stage, support vector machines (SVM) with three linear kernels, polynomial kernel and Gaussian kernel have been used as classifier. We tested our algorithm on the dataset that contained 8600 samples of Persian handwritten digits for performance analysis. Using 8000 samples in learning stage and another 600 samples in testing stage. The results got with use of every three kernels of support vector machine and achieved maximum accuracy by using Gaussian kernel with gamma equal to 0.16. In pre-processing stage only image binarization is used and all the images of this dataset had been normalized at center with size 40x40. The recognition rate of this method, on the test dataset 97.83% and on all samples of dataset 100% was earned.

关键词： pattern recognition Optical character recognition Support vector machine Persian handwritten digits

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