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A kernelized maximal-figure-of-merit learning approach based on subspace distance minimization
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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We propose a kernelized maximal-figure-of-merit (MFoM) learning approach to efficiently training a nonlinear model using subspace distance minimization. ...
We show that the subspace distance can be minimized through the Nyström extension. ...
To tackle this issue, thus, we propose a kernelized MFoM learning approach based on a subspace distance minimization criterion. ...
doi:10.1109/icassp.2011.5946732
dblp:conf/icassp/ByunL11
fatcat:4tt2iyud6fcyvkbwiwt37w33re
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach
[article]
2018
arXiv
pre-print
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Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach. ...
The proposed method inherits the merits of the subspace projection method called domain regularized component analysis. ...
Zhang for his clear and helpful explanation on the DCRA method. ...
arXiv:1901.02321v1
fatcat:nxwuyf7f45ew3n324ftfrcyjji
Anti-Drift in Electronic Nose via Dimensionality Reduction: A Discriminative Subspace Projection Approach
2019
IEEE Access
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The proposed method has multiple properties. (1) It inherits the merits of the subspace projection approach called domain regularized component analysis via introducing a regularization parameter to tackle ...
Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach. ...
Zhang for his clear and helpful explanation on the DRCA method. ...
doi:10.1109/access.2019.2955712
fatcat:zslr7pocyrdppexhlyfaeyhv44
Discriminant analysis on Riemannian manifold of Gaussian distributions for face recognition with image sets
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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To encode such Riemannian geometry properly, we investigate several distances between Gaussians and further derive a series of provably positive definite probabilistic kernels. ...
In the light of information geometry, the Gaussians lie on a specific Riemannian manifold. ...
Figure 1 : 1 Conceptual illustration of the proposed approach. (a) Training image sets in the gallery. ...
doi:10.1109/cvpr.2015.7298816
dblp:conf/cvpr/WangWHSC15
fatcat:xw7anblsjra6hnw6vi4ks6vwje
Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition with Image Sets
2017
IEEE Transactions on Image Processing
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To encode such Riemannian geometry properly, we investigate several distances between Gaussians and further derive a series of provably positive definite probabilistic kernels. ...
In the light of information geometry, the Gaussians lie on a specific Riemannian manifold. ...
Figure 1 : 1 Conceptual illustration of the proposed approach. (a) Training image sets in the gallery. ...
doi:10.1109/tip.2017.2746993
pmid:28866497
fatcat:h4nu5sufprg4xlii2kga3xdj4u
A Survey on Multi-view Learning
[article]
2013
arXiv
pre-print
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Notably, co-training style algorithms train alternately to maximize the mutual agreement on two distinct views of the data; multiple kernel learning algorithms exploit kernels that naturally correspond ...
to different views and combine kernels either linearly or non-linearly to improve learning performance; and subspace learning algorithms aim to obtain a latent subspace shared by multiple views by assuming ...
Subspace Learning-based Approaches Subspace learning-based approaches aim to obtain a latent subspace shared by multiple views by assuming that the input views are generated from this subspace. ...
arXiv:1304.5634v1
fatcat:nnux76pyobdzhovzlcywxrzkty
Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold
2022
Brain Sciences
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In KMDA, the covariance matrices are aligned in the Riemannian manifold, and then are mapped to a high dimensional space by a log-Euclidean metric Gaussian kernel, where subspace learning is performed ...
Methods: In this paper, we propose a novel domain adaptation framework, referred to as kernel-based Riemannian manifold domain adaptation (KMDA). ...
Conflicts of Interest: The authors declare no conflict of interest regarding the publication of this article. ...
doi:10.3390/brainsci12050659
fatcat:uwrzer2fuzdjtebiijjsam4jbu
Manifold elastic net for sparse learning
2009
2009 IEEE International Conference on Systems, Man and Cybernetics
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MEN combines merits of the manifold regularization and the elastic net regularization, so it considers both the nonlinear manifold structure of a dataset and the sparse property of the redundant data representation ...
Most of existing works apply the appearance based information for data representation. A face image with size 40 by 40 could be seen as a point in a linear space with 1600 dimensions. ...
Dimension on FERET and UMIST
Figure 3 . 3 Recognition rate vs. number of training samples on FERET
Figure 4 . 4 Recognition rate vs. number of training samples on
Figure 6 .Figure 8 . 68 5 bases ...
doi:10.1109/icsmc.2009.5346879
dblp:conf/smc/ZhouT09
fatcat:x6a2h363z5fcpe7pvgjtelo7zi
Scalable Outlying-Inlying Aspects Discovery via Feature Ranking
[chapter]
2015
Lecture Notes in Computer Science
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Second, we present OARank -a hybrid framework that leverages the efficiency of feature selection based approaches and the effectiveness and versatility of score-and-search based methods. ...
Our proposed approach is orders of magnitudes faster than previously proposed score-and-search based approaches while being slightly more effective, making it suitable for mining large data sets. ...
The average Jaccard index and precision over all outliers for different approaches on all datasets are reported in Figure 3(a,b) . ...
doi:10.1007/978-3-319-18032-8_33
fatcat:kwktelktdzh2zgmxdxnfqfj3de
Face Subspace Learning
[chapter]
2011
Handbook of Face Recognition
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The last few decades have witnessed a great success of subspace learning for face recognition. ...
Mathematically, PCA maximizes the variance in the projected subspace for a given dimensionality, decorrelates the training face images in the projected subspace, and maximizes the mutual information between ...
Li for insightful discussions on nearest feature line. ...
doi:10.1007/978-0-85729-932-1_3
fatcat:ot7fkakworamtavm4jwlp4sfjm
Kernel-based distance metric learning in the output space
2013
The 2013 International Joint Conference on Neural Networks (IJCNN)
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2013 pre-print arXiv:1312.2578v1 fatcat:yk7nbb6tljachg3kd6i3xozuuqOther Versions
In this paper we present two related, kernel-based Distance Metric Learning (DML) methods. ...
Experimental results for a collection of classification tasks illustrate the advantages of the proposed methods over other traditional and kernel-based DML approaches. ...
Eventually, one can simultaneously learn the output space distance metric and the mapping f through a joint minimization. ...
doi:10.1109/ijcnn.2013.6706862
dblp:conf/ijcnn/LiGA13
fatcat:jz7kksq35vdpzbfvor4ch2dcnu
Margin Based Semi-Supervised Elastic Embedding for Face Image Analysis
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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Unlike many state-of-the art non-linear embedding approaches which suffer from the out-of-sample problem, our proposed methods have a direct out-of-sample extension to novel samples. ...
are based on label propagation or graph-based semi-supervised embedding. ...
In [10] , the authors propose a joint learning of labels and distance metric approach, which is able to optimize the labels of unlabeled samples and a Mahalanobis distance metric in a unified scheme. ...
doi:10.1109/iccvw.2017.156
dblp:conf/iccvw/DornaikaT17
fatcat:pbmr2pz5erd27f5skd2xhw2c3e
Localizing volumetric motion for action recognition in realistic videos
2009
Proceedings of the seventeen ACM international conference on Multimedia - MM '09
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Experiments on a realistic Hollywood movie dataset show that the proposed approach can achieve 20% relative improvement compared to the state-ofthe-art STIP based algorithm. ...
Previous works mainly focus on learning from descriptors of cuboids around space time interest points (STIP) to characterize actions. ...
Figure 3 : 3 VOI extraction and description, (a) low dimensional embedding of trajectories, (b) determination of spatiotemporal boundary of 3D VOI by using the minimal and maximal coordinates of trajectories ...
doi:10.1145/1631272.1631342
dblp:conf/mm/WuNLZ09
fatcat:fxnhowv4urbwhc3vlqkhuuhnwq
Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics
2015
Mathematical Problems in Engineering
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In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP) is proposed in this paper, which can maximize the class separability in kernel learning. ...
However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP. ...
In SKOLPP, we first construct a data-dependent kernel [18] to maximize the class separability based Fisher Criterion. ...
doi:10.1155/2015/421671
fatcat:htlmlqak6rbhjfhw4etaku75rm
FISH-MML: Fisher-HSIC Multi-View Metric Learning
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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In our approach, the class separability is enforced in the spirit of FDA within each single view, while the consistence among different views is enhanced based on HSIC. ...
learning method based on Fisher discriminant analysis (FDA) and Hilbert-Schmidt Independence Criteria (HSIC), termed as Fisher-HSIC Multi-View Metric Learning (FISH-MML). ...
Acknowledgments This work was supported in part by National Natural Science Foundation of China (Grand No:61602337, 61732011, 61702358). ...
doi:10.24963/ijcai.2018/424
dblp:conf/ijcai/ZhangLLHLZ18
fatcat:s7byv55zuncytaud2uwz33fsfa
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