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Ensemble-Based Discriminant Manifold Learning for Face Recognition
[chapter]
2006
Lecture Notes in Computer Science
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Based on the proposed ULLELDA (Unified LLE and linear discriminant analysis) algorithm, an ensemble version of the ULLELDA (En-ULLELDA) is proposed by perturbing the neighbor factors of the LLE algorithm ...
The locally linear embedding (LLE) algorithm can be used to discover a low-dimensional subspace from face manifolds. ...
However, when the manifold learning-based approaches are employed for discriminant learning, the accuracy of classifiers easily suffer. ...
doi:10.1007/11881070_5
fatcat:wjt6lftxzngldm5zgv4amgcv4q
Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning
[article]
2018
arXiv
pre-print
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We mitigate this by introducing ensemble manifold segmentation (EMS). ...
EMS accounts for the manifold structure by dividing the training data into an ensemble of classification training sets that contain samples of local proximity. ...
Manifold-based Methods. We compare with manifold-based methods on four classification datasets: STL-10, CIFAR-100, Indoor-67 [29] and SUN-397 [47] . ...
arXiv:1804.02201v1
fatcat:pdcy5tyoorf23kmnsa6blpmgty
Dimensionality Reduction Ensembles
[article]
2017
arXiv
pre-print
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This study explores dimensionality reduction ensembles, using principal component analysis and manifold learning techniques to capture linear, nonlinear, local, and global features in the original dataset ...
Dimensionality reduction ensembles are tested first on simulation data and then on two real medical datasets using random forest classifiers; results suggest the efficacy of this approach, with accuracies ...
Results
I) Simulations Across simulations, the full dataset random forest classifier attains the best accuracy, followed by the small ensemble classifier and the large ensemble classifier (Figure 1) ...
arXiv:1710.04484v1
fatcat:rdlg42p54jdgde35zrrcuc7mwa
Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks
2014
Sensors
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More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. ...
To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. ...
classifiers for ensemble, our method formulates the ensemble as an optimal base classifier selection, which just selects appropriate base classifiers for integration. ...
doi:10.3390/s141223509
pmid:25494350
pmcid:PMC4299075
fatcat:ai2n344vrnh7pftgiejab4hpsm
Envelope imbalanced ensemble model with deep sample learning and local-global structure consistency
[article]
2022
arXiv
pre-print
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After that, base classifiers are applied on the layers of deep samples respectively.Finally, the predictive results from base classifiers are fused through bagging ensemble learning mechanism. ...
Based on the analysis above, an imbalanced ensemble algorithm with the deep sample pre-envelope network (DSEN) and local-global structure consistency mechanism (LGSCM) is proposed here to solve the problem.This ...
[34] propose a hybrid optimal ensemble classifier framework with a density-based undersampling method. ...
arXiv:2206.13507v1
fatcat:b2pot2sqsnhbbavhsz352xkb44
Statistical shape model for manifold regularization: Gleason grading of prostate histology
2013
Computer Vision and Image Understanding
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model of manifolds (SSMM). ...
In this paper we present a manifold regularization technique to constrain the low dimensional manifold to a specific range of possible manifold shapes, the range being determined via a statistical shape ...
In Bagging, an ensemble classifier is constructed from a set of weak classifiers. ...
doi:10.1016/j.cviu.2012.11.011
pmid:23888106
pmcid:PMC3718190
fatcat:7lsgngbevvcmrejt6rw3wrdsty
A Semisupervised Concept Drift Adaptation via Prototype-Based Manifold Regularization Approach with Knowledge Transfer
2023
Mathematics
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This study also proposes an ensemble-based concept drift adaptation approach that transfers reliable classifiers to the new concept. ...
Based on the results, this proposed approach can detect concept drifts and fully supervised data stream mining approaches and performs well on mixed-severity concept drift datasets. ...
This can be achieved by ensemble-based approaches that prune the underperforming classifiers [28, 29] . ...
doi:10.3390/math11020355
fatcat:n5bk3xltbbbudlq3jp55wlgmgq
Improved Audio Classification Using A Novel Non-Linear Dimensionality Reduction Ensemble Approach
2013
Zenodo
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from different t-SNE based classifiers with various target space dimensionalities) (c) Ensemble composed of 5 classifiers obtained using 1 to 5dimensional feature sets obtained through t-SNE on the full ...
from different t-SNE based classifiers with various target space dimensionalities) (c) Ensemble composed of 5 classifiers obtained using 1 to 5dimensional feature sets obtained through t-SNE on the full ...
doi:10.5281/zenodo.1417704
fatcat:25wgactf5jb7hpnycvgtyn53di
Machine learning and intelligence science: Sino-foreign interchange workshop IScIDE2010 (A)
2011
Frontiers of Electrical and Electronic Engineering in China
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Besides the disagreement-based semi-supervised learning, another category has witnessed a surge of interests, which is graph-based or manifold-based semi-supervised learning. ...
Based on his expertise, Zhou further suggests that disagreement-based methods provide a good vessel to accommodate advantages not just from all semi-supervised learning categories but also from classifier ...
doi:10.1007/s11460-011-0136-0
fatcat:tsrdewj6s5brtnkfo4omuyuu2u
Ensemble manifold regularization
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
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As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. ...
Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold ...
The manifold approximation and the classifier learning is combined together under the conventional regularization framework [6] , which smoothes the classifier along the manifold. ...
doi:10.1109/cvprw.2009.5206695
fatcat:it6474gfbfarno2d5cfirdmga4
Ensemble manifold regularization
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
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As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. ...
Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold ...
The manifold approximation and the classifier learning is combined together under the conventional regularization framework [6] , which smoothes the classifier along the manifold. ...
doi:10.1109/cvpr.2009.5206695
dblp:conf/cvpr/GengXTYH09
fatcat:ocihfweacjbunbllcarahgz2ca
Ensemble Manifold Regularization
2012
IEEE Transactions on Pattern Analysis and Machine Intelligence
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As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. ...
Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold ...
The manifold approximation and the classifier learning is combined together under the conventional regularization framework [6] , which smoothes the classifier along the manifold. ...
doi:10.1109/tpami.2012.57
pmid:22371429
fatcat:jnp72v6q3vebvhpmwjxgcu3ihi
Recognizing coordinated multi-object activities using a dynamic event ensemble model
2009
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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An appropriate classifier on the manifold is then designed for recognizing new activities. Experiments on football play recognition demonstrate the effectiveness of the framework. ...
In particular, we exploit the Riemannian geometric property of the set of ensemble description functions and develop a compact representation for group activities on the ensemble manifold. ...
For clarity we call this manifold the event ensemble manifold. ...
doi:10.1109/icassp.2009.4960390
dblp:conf/icassp/LiC09
fatcat:xka6zb25i5aunp2vkechixll54
Semi-supervised Spectral Clustering for Image Set Classification
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
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Image sets are compactly represented with multiple Grassmannian manifolds which are subsequently embedded in Euclidean space with the proposed spectral clustering algorithm. ...
We present an image set classification algorithm based on unsupervised clustering of labeled training and unlabeled test data where labels are only used in the stopping criterion. ...
Ensemble of Spectral Classifiers Representing image sets with Grassmannian manifolds facilitates formulation of an ensemble of spectral classifiers. ...
doi:10.1109/cvpr.2014.23
dblp:conf/cvpr/MahmoodMO14
fatcat:gmls7q2w7bcsxfllhzgzwyim2e
Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection
2022
Remote Sensing
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By exploiting ensemble clustering, more effective clustering results are expected based on multiple band partitions given by base clustering with different parameters. ...
To tackle such issues, in this paper, we propose a correlation-guided ensemble clustering approach for hyperspectral band selection. ...
Generally, ensemble clustering can be classified into objective function-based and heuristic-based methods [30] . ...
doi:10.3390/rs14051156
fatcat:55sm2vmwxbf6dkkrpz5p7y5qka
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