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A collaborative representation based projections method for feature extraction
2015
Pattern Recognition
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In graph embedding based methods, we usually need to manually choose the nearest neighbors and then compute the edge weights using the nearest neighbors via L2 norm (e.g. LLE). ...
CRP is much faster than SPP since CRP calculates the objective function with L2 norm while SPP calculate the objective function with L1 norm. ...
In CRP, for classification, we consider two quantities: local information and global information in the modeling process. The local quantity is characterized via the L2 norm graph. ...
doi:10.1016/j.patcog.2014.07.009
fatcat:uyl4kfqhzfb7rbh7rqcerklio4
Learning efficient structured dictionary for image classification
[article]
2020
arXiv
pre-print
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In this paper, we present an efficient structured dictionary learning (ESDL) method which takes both the diversity and label information of training samples into account. ...
Moreover, in contrast with conventional DL approaches which impose computationally expensive L1-norm constraint on the coefficient matrix, ESDL employs L2-norm regularization term. ...
Fig 10 10 Recognition accuracy of ESDL versus parameters α and γ on the LFW database. 20 We proposed an efficient structured dictionary learning (ESDL) method in which both the diversity and label information ...
arXiv:2002.03271v2
fatcat:knxm7mcxebgurihjmxigwl333m
Simultaneous Object Recognition and Localization in Image Collections
2010
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
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via two different types of support vector machines: the standard L2regularized L1-loss SVM, and the one with L1 regularization and L2 loss. ...
We show that the use of our visual attention maps improves the recognition performance, while the one selected by L1-regularized L2-loss SVMs exhibits the best recognition and localization results. ...
Acknowledgments This work is supported by NSC98-2218-E-001-004 and NSC99-2631-H-001-018. ...
doi:10.1109/avss.2010.47
dblp:conf/avss/WangW10
fatcat:7tllmbyojncsfiss4ux4gezhta
A Novel Approach for Stable Selection of Informative Redundant Features from High Dimensional fMRI Data
[article]
2016
arXiv
pre-print
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In order to improve the stability, generalization and interpretations of the discovered potential biomarker and enhance the robustness of the resultant classifier, the redundant but informative features ...
However, traditional multivariate methods is likely to obtain unstable and unreliable results in case of an extremely high dimensional feature space and very limited training samples, where the features ...
our method together with L2-SVM are the only two methods which can find out the accurately discriminative regions. ...
arXiv:1506.08301v2
fatcat:l5rhio2wbjdj7esmfmoebbcks4
l2, 1 Regularized correntropy for robust feature selection
2012
2012 IEEE Conference on Computer Vision and Pattern Recognition
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Extensive experiments show that our method can select robust and sparse features, and outperforms several state-of-the-art subspace methods on largescale and open face recognition datasets. ...
In terms of face recognition, we apply the proposed method to obtain an appearance-based model, called Sparse-Fisherfaces. ...
Since discriminative LPP is based on a local structure which depends on label information, it is sensitive to mislabeling noise. ...
doi:10.1109/cvpr.2012.6247966
dblp:conf/cvpr/HeTWZ12
fatcat:b2cafx7n7ngndnie4g4drfg7ue
Structure Preserving Low-Rank Representation for Semi-supervised Face Recognition
[chapter]
2013
Lecture Notes in Computer Science
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Constructing an informative and discriminative graph plays an important role in the graph based semi-supervised learning methods. ...
Among these graph construction methods, low-rank representation based graph, which calculates the edge weights of both labeled and unlabeled samples as the low-rank representation (LRR) coefficients, has ...
As a result, the proposed model derives a informative graph and shows the best performance in the comparison with state-of-the-art methods for semi-supervised face recognition. ...
doi:10.1007/978-3-642-42042-9_19
fatcat:zfp3k66a2rge7dpcri5yyzb6my
Dimension reduction using collaborative representation reconstruction based projections
2016
Neurocomputing
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KPCA is nonlinear version of PCA via kernel tricks and could handle implicitly deal with the nonlinear data. KFLDA is the nonlinear version of FLDA via kernel tricks. J. Yang ...
So the proposed method is called CRRP. The experimental results on AR, Yale B and CMU PIE face databases demonstrate that CRRP is an effective dimension reduction method. ...
CRC is a regularized least square regression with L2-norm constraint. C. Ren et al. [41] proposed a L21-norm-based regression 3 based classification method. F. Shen et al. ...
doi:10.1016/j.neucom.2016.01.060
fatcat:umdatgweyndchdb7nzwwerp6qa
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1803.09014v1 fatcat:xawlj3t46vbsdl7iqgju6sxm2a
Despite the large volume of face recognition datasets, there is a significant portion of subjects, of which the samples are insufficient and thus under-represented. ...
Advantageous results on LFW, IJB-A and MS-Celeb-1M demonstrate the effectiveness of our feature transfer and training strategy, compared to both general baselines and state-of-the-art methods. ...
Train↓ Method↓ Regular Long-tail Regular Long-tail Regular Long-tail Test →
MS1M: FC
MS1M: NN
Weight Norm
10K0K sfmx+m-L2 92.03
-
90.21
84.64
0.427
-
10K10K
sfmx+m-L2 90.76
0.15
89.48
84.10 ...
arXiv:1803.09014v2
fatcat:jnyxg7qxwralvprypm2txk6xdi
Sparse Network-Based Models for Patient Classification Using fMRI
2013
2013 International Workshop on Pattern Recognition in Neuroimaging
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Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). ...
gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. ...
Introduction Recent research using pattern recognition methods applied to whole-brain neuroimaging data, such as structural/functional Magnetic Resonance Imaging (s/fMRI) data, has proved successful at ...
doi:10.1109/prni.2013.26
dblp:conf/prni/RosaPSM13
fatcat:sqyrhj7jubgm5fmfafxlidnjla
Sparse network-based models for patient classification using fMRI
2015
NeuroImage
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Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). ...
gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. ...
Introduction Recent research using pattern recognition methods applied to whole-brain neuroimaging data, such as structural/functional Magnetic Resonance Imaging (s/fMRI) data, has proved successful at ...
doi:10.1016/j.neuroimage.2014.11.021
pmid:25463459
pmcid:PMC4275574
fatcat:w3wdj2ll7jamlcy5hfyjjbag34
L2-constrained Softmax Loss for Discriminative Face Verification
[article]
2017
arXiv
pre-print
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A typical pipeline for face verification includes training a deep network for subject classification with softmax loss, using the penultimate layer output as the feature descriptor, and generating a cosine ...
Additionally, we achieve state-of-the-art performance on LFW dataset with an accuracy of 99.78%, and competing performance on YTF dataset with accuracy of 96.08%. ...
(a) Face Verification Performance on IJB-A dataset. The templates are divided into 3 sets based on their L2-norm. '1' denotes the set with low L2-norm while '3' represents high L2-norm. ...
arXiv:1703.09507v3
fatcat:osuh5c62c5g7tcfd5ijhbvrilu
Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition
2013
2013 IEEE International Conference on Computer Vision
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Finally, we conduct face identification on CMU Multi-PIE, and verification on Labeled Faces in the Wild (LFW) databases, where identification rank-1 accuracy and face verification accuracy with ROC curve ...
One of the most challenging task in face recognition is to identify people with varied poses. Namely, the test faces have significantly different poses compared with the registered faces. ...
This research is supported in part by the NSF CNS award 1314484, Office of Naval Research award N00014-12-1-0125 and N00014-12-1-1028, Air Force Office of Scientific Research award FA9550-12-1-0201, and ...
doi:10.1109/iccv.2013.300
dblp:conf/iccv/ZhangSWF13
fatcat:qyt77qgmabhvvjntaa3g2o3hnm
Robust human face recognition based on locality preserving sparse over complete block approximation
2014
Media Watermarking, Security, and Forensics 2014
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Compressive Sensing (CS) has become one of the standard methods in face recognition due to the success of the family of Sparse Representation based Classification (SRC) algorithms. ...
We compare two image representations using a pixel-wise approximation and an overcomplete block-wise approximation with two types of sparsity priors. ...
The authors are thankful to Maurits Diephuis for the discussions on the links to the deep learning encoding algorithms and his comments in the early versions of the paper. ...
doi:10.1117/12.2042506
dblp:conf/mediaforensics/KostadinovVF14
fatcat:zx6d2olkozgxvo7mhosn324mkm
Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification
2021
Applied Sciences
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Moreover, all the introduced constraints in the proposed LPLC-HDL method are based on the l2-norm regularization, which can be solved efficiently via employing an alternative optimization strategy. ...
In this paper, we proposed a locality preserving and label-aware constraint-based hybrid dictionary learning (LPLC-HDL) method, and apply it in image classification effectively. ...
make the coding coefficients V p have a block-diagonal structure with strong discriminative information. ...
doi:10.3390/app11167701
fatcat:wzcundxrpze4xj7yyawum6auda
Graph Regularized Nonnegative Matrix Factorization with Sparse Coding
2015
Mathematical Problems in Engineering
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Experimental results demonstrate encouraging results of GRNMF_SC on image recognition and clustering when comparing with the other state-of-the-art NMF methods. ...
In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF_SC). ...
Acknowledgments Chuang Lin acknowledges the financial support of Natural Science Foundation of China (no. 61272371) and Fundamental Research Funds for the Central Universities, Dalian University of Technology ...
doi:10.1155/2015/239589
fatcat:mvc2iqvrt5a5jd3m2l2qrgq7ie
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