linear discriminant analysis

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Construction and update of an online ensemble score involving linear discriminant analysis and logistic regression

Construction and update of an online ensemble score involving linear discriminant analysis and logistic regression

5 2.2 Updating the predictors Recursive stochastic approximation algorithms which take into account a mini-batch of new data at each step can be used to update the predictors. Such algorithms have been developed to estimate linear [8] or logistic [9] regression parameters, or to estimate the class centers in unsupervised classification [10] or the principal components of a factor analysis [11]. These algorithms do not require storing data and can, within a fixed timeframe, process more data than offline methods. Stochastic approximation algorithms able to update predictors obtained by linear discriminant analysis (LDA, equivalent to linear regression in the case of a binary dependent variable) and logistic regression (LR) are described below.
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Relative efficiency of non parametric error rate estimators in multi-group linear discriminant analysis

Relative efficiency of non parametric error rate estimators in multi-group linear discriminant analysis

dered in (1.1) as the estimated pooled covariance matrix of the k populations. Whatever the rule established is, it is subject to a probability of misclassifications. Then, an actual error rate is associated with any classification rule established on data samples in order to evaluate its efficiency. In practice, it is impossible to precisely determine the actual error rate, because it is only computed on the actual parameters of the populations, which are usually unknown. To solve this problem, some parametric and non parametric estimators of the actual error rate were established (McLachlan, 1992). Parametric estimators were established for two normal homoscedastic groups and estimated the actual error rate, using some para- meters related to the considered samples such as the estimated Mahalanobis distance between the two groups. On the contrary, non-parametric error rate estimators do not depend on any hypothesis of use and were based on resampling methods. For two-group discriminant analysis, many comparison studies of error rate esti- mators have been done in linear discriminant analysis, in order to deduce the ones that have the lowest errors compared with the theoretical actual error rate. A thorough review of these studies was provided by Schiavo and Hand (2000). However, in real world problems, more than two groups are often considered in discriminant analysis. This paper evaluated and com- pared by simulation technique, the efficiency of ten non parametric error rate estimators for 2, 3 and 5 groups submitted to linear discriminant analysis.
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Shrinkage parameter for modified linear discriminant analysis

Shrinkage parameter for modified linear discriminant analysis

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TENSOR OBJECT CLASSIFICATION VIA MULTILINEAR DISCRIMINANT ANALYSIS NETWORK

TENSOR OBJECT CLASSIFICATION VIA MULTILINEAR DISCRIMINANT ANALYSIS NETWORK

E-mail: zengrui@seu.edu.cn.jswu@seu.edu.cn.lotfi.senhadji@univ-rennesI.fr.shu.list@seu.edu.cn ABSTRACT This paper proposes an multilinear discriminant analysis net­ work (MLDANet) for the recognition of multidimensional objects, knows as tensor objects. The MLDANet is a variation of linear discriminant analysis network (LDANet) and prin­ cipal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms. The ML­ DANet consists of three parts: 1) The encoder learned by MLDA from tensor data. 2) Features maps obtained from de­
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Mapping natural habitats using remote sensing and Sparse partial least square discriminant analysis

Mapping natural habitats using remote sensing and Sparse partial least square discriminant analysis

The selected variables that strongly contribute to the discrimination of the seven classes of habitats are shown in Figure 7 and are highlighted by high correlation values with the latent variables. We notice that the variables that mostly contribute to the separation of the habitats are essentially the spectral features of the RapidEye images in 2009 and 2010 (e.g. Mean, brightness and STD) as well as the textural features mainly in the NIR bands of the two im- ages. Curiously, the features derived from the NDVI and the Red band were rarely selected by the SPLSDA and seem to be less important for the discrimination of the habitat classes present in the study area. Temporal variations of the NDVI could help in distinguishing im- proved grasslands when the NDVI is calculated from different observation dates in the annual cycle (Lucas et al. 2007). The two RapidEye images used in our analysis were acquired dur- ing the summer season of 2009 and 2010. Usually, year to year reflectance of the landscape should be relatively similar in the summer period as many vegetation coversare close to max- imum production. This possibly explains the lack of significant contribution of the NDVI to the discrimination of the habitat classes.
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Apprentissage discriminant de modèles neuronaux pour la traduction automatique

Apprentissage discriminant de modèles neuronaux pour la traduction automatique

Figure 4. La couche structurée en arbre du modèle SOUL permettant le calcul efficace de la probabilité d’un mot dans son contexte. des forces de ce type d’approches est de pouvoir se dispenser des alignements de mots, et de la définition préalable des unités de traduction (voir toutefois (Bahdanau et al., 2014) qui réintroduit une forme d’alignement à travers les modèles d’attention). Néanmoins, ces modèles sont optimisés de manière à maximiser la vraisemblance mesurée sur les données d’apprentissage. Une extension intéressante serait de propo- ser un cadre discriminant d’apprentissage pour ce type d’approches afin de les rendre sensibles aux métriques d’évaluation utilisées en TAS.
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Comparison of PLS and SVM discriminant analysis for NIR hyperspectral data of wheat roots in soil

Comparison of PLS and SVM discriminant analysis for NIR hyperspectral data of wheat roots in soil

Dale, L.M., Thewis, A., Rotar, I., Fernández Pierna, J.A., Boudry, C., Vidican, R.M., Baeten, V., 2012. Chemometric tools for NIRS and NIR Hypespectral Imaging. Bulletin UASVM Agriculture. 69 (1), pp. 70-76. Eylenbosch, D., Fernandez Pierna, J. A., Baeten, V., Bodson, B., 2014. Detection of wheat root and straw in soil by use of NIR hyperspectral imaging spectroscopy and Partial Least Square discriminant analysis, in: proceedings of the ESA XIIIth Congress, Debrecen, Hungary, pp. 237-238. Fernández Pierna, J. A. , Baeten, V., Michotte Renier, A., et al., 2004. Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds. J Chemometr. 18, pp. 341-349.
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Reflection Analysis of Non-linear Regular Waves.

Reflection Analysis of Non-linear Regular Waves.

L’accès à ce site Web et l’utilisation de son contenu sont assujettis aux conditions présentées dans le site LISEZ CES CONDITIONS ATTENTIVEMENT AVANT D’UTILISER CE SITE WEB. NRC Publicat[r]

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Analysis of linear homing missle control systems

Analysis of linear homing missle control systems

Analytical techniques have been developed for handling this particular control system on the basis of miss distances for the cases where the missile can be represe[r]

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The analysis and synthesis of linear servomechanisms

The analysis and synthesis of linear servomechanisms

This conclusion results from the fact that the transfer-locus of the original Type I servo (curve A, figure 57) approaches the origin along the negative real axis, and[r]

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HDclassif: an R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

HDclassif: an R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

1. Introduction Classification in high-dimensional spaces is a recurrent problem in many fields of science, for instance in image analysis or in spectrometry. Indeed, the data used in these fields are often high-dimensional and this penalizes most of the classification methods. In this paper, we focus on model-based approaches. We refer to Bock ( 1996 ) for a review on this topic. In this context, popular classification methods are based on the Gaussian mixture model ( McLachlan and Peel 2000 ) and show a disappointing behavior when the size of the dataset is too small compared to the number of parameters to estimate. This well-known phenomenon is called the curse of dimensionality and was first identified by
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On the Analysis of Linear Probing Hashing

On the Analysis of Linear Probing Hashing

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r]

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Mapping natural habitats using remote sensing and sparse partial least square discriminant analysis

Mapping natural habitats using remote sensing and sparse partial least square discriminant analysis

b) Grasses c) Forest a) Garrigues thematic features (derived from the TPI) features were calculated and used as variables for input to the SPLSDA. Given the large number of habitat classes to identify (15 classes in total) and the difficulty in interpreting the visual and numerical results of the SPLSDA, the analysis of the sample dataset was conducted through a stepwise procedure. In a first step, the samples were grouped into three broad physiognomic categories of vegetation: a “Garrigues” group including all habitats that correspond to low, soft-leaved scrubs (31.812; 32.11; 32.64; 32.162; 32.A), a “Grasses” group including habitats with a herbaceous vegetation cover mainly composed of graminea (34.332, 34.36, 34.721, 38.22, 81.1) and a “Forest” group representing habitats with a high density of trees (41.714, 41.9, 42.67, 44.63, 45.313). Such a grouping of habitat classes is based on the hypothesis that physiognomic characteristics of vegetation can be readily distinguished in remote sensing data due to the signature of the dominant communities. In a second step, SPLSDA analysis is run sequentially on each of the three groups of habitats to analyze the within-group separability and to see which classes of habitats can to be considered separately from the rest of the physiognomic group. At each step which is the number of dimensions or variates to choose was set to where is the number of classes of the group. For selecting the number of variables to keep on each dimension, we plotted the mean classification error rate (10-cross-validation averaged 10 times) for each SPLSDA dimension. The estimated average error rate is an indication on the optimal number of variables to be selected. Once the most discriminant variables have been selected, the next step has been to run SPLSDA model on all the image objects resulting from the segmentation that fall within the area of investigation to produce the final classification. The accuracy of the map of predicted habitat classes was finally analyzed using the evaluation set. All the methods were implemented in R statistical software.
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Quelques techniques pour l'amélioration du pouvoir discriminant de primitives discrètes

Quelques techniques pour l'amélioration du pouvoir discriminant de primitives discrètes

frederic.grandidier@iutsd.uhp-nancy.fr Robert.Sabourin@etsmtl.ca suen@cenparmi.concordia.ca Résumé : Cet article décrit plusieurs méthodes permettant l’amélioration du pouvoir discriminant d’ensembles de pri- mitives discrètes. En tirant partie des possibilités offertes par la modélisation markovienne des caractères, nous proposons une technique permettant l’application de l’algorithme LDA sur nos données. Différentes méthodes de segmentation de la zone d’intérêt lors de l’extraction des caractéristiques sont présentées. Finalement une stratégie de pondération de ces différentes zones est définie. Elle permet d’intégrer l’infor- mation concernant le style d’écriture de l’échantillon traité. Les résultats expérimentaux obtenus montrent l’intérêt des différentes stratégies.
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Seismic solar models from Ledoux discriminant inversions

Seismic solar models from Ledoux discriminant inversions

The approach used to connect the regions on which the A correc- tions are applied to the central regions may also locally impact the procedure. Obviously, the fact that no corrections are applied below 0.08 R ⊙ leaves a direct mark on the final reconstructed structure. This is illustrated in Fig. 8 and Fig 9 where we plot the Ledoux discriminant, Brunt-Väisälä frequency and sound-speed profiles of all our reconstructed models in the deep solar core. From the inspection of the right panel of Fig. 8, we can under- stand better the behaviour of the period spacing changes of table 2. Indeed, Model 1 and 7 show some minor changes in asymp- totic period spacing, while the spacing of Model 5 is significantly corrected. This is simply due to the fact that Model 1 and 7 re- produce much better the Brunt-Väisälä frequency of the Sun in the deep radiative layers. However, as we lack constraints below 0.08 R ⊙ , we cannot state with full confidence that the observed period spacing will lay within the 25 s range we find.
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The discriminant role of mechanics during cell migration

The discriminant role of mechanics during cell migration

Any correspondence concerning this service should be sent to the repository Administrator : archiveouverte@ensam.eu.. During the last decades several numerical works have been p[r]

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Using quadratic discriminant analysis for osteometric pair‐matching of long bone antimeres: An evaluation on modern and archaeological samples

Using quadratic discriminant analysis for osteometric pair‐matching of long bone antimeres: An evaluation on modern and archaeological samples

We present here an approach based on quadratic discriminant analysis (QDA). This approach is evaluated on antimeric pairs of humeri and femora from the openly available Goldman Data Set, and compared to two classical and previously published methods for osteometric pair-matching, based respectively on linear regressions and t-tests. It is shown that QDA globally outperforms existing solutions for reassociating those long bones, in particular by rejecting fewer true bone pairs at the classical α level of 0.10. The accuracy of all three methods is analyzed through receiver operating characteristic (ROC) curves to assess the influence of the choice of a decision threshold. The application on archaeological commingled remains of pair-matching models learned on a modern reference multipopulation sample is discussed. Finally, an R package containing the func- tions used for this study, bonepairs, is publicly available online. This ensures the full replicability of results and an easy use of the new method introduced here.
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Apprentissage discriminant des GMM à grande marge pour la vérification automatique du locuteur

Apprentissage discriminant des GMM à grande marge pour la vérification automatique du locuteur

Factor Analysis (SFA) [7, 8]. Le SFA consiste en un apprentis- sage g´en´eratif de GMM permettant de compenser la variabilit´e inter-sessions. Notre algorithme est bas´ee sur deux propri´et´es : la d´ecision de classification utilise g´en´eralement uniquement les k-meilleures gaussiennes, et la correspondance entre les composantes des GMM appris par adaptation MAP [1] et celles du mod`ele du monde (UBM).

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Combining Discriminant Models with new Multi-Class SVMs

Combining Discriminant Models with new Multi-Class SVMs

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Apprentissage discriminant sur des graphes pour la caractérisation des paysages propices à l'étalement urbain

Apprentissage discriminant sur des graphes pour la caractérisation des paysages propices à l'étalement urbain

Dans tous les cas, on obtient pour chaque zone un sous-graphe dont les nœuds sont les nœuds associés aux parcelles que la zone contient et les arêtes sont toutes les arêtes entre les dif[r]

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