Maximum Neighborhood Margin Discriminant Projection for Classification
The Scientific World JOURNALPublished 1 January 2014Open access
Jianping Gou, Yongzhao Zhan, Min Wan, Xiang‐Jun Shen, Jinfu Chen, Lan Du
Citations745
SJR quartileQ2
SJR score0.65
SNIP1.24
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A novel maximum neighborhood margin discriminant projection technique for dimensionality reduction of high-dimensional data that cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes.
Abstract
We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.
Keywords
Computer ScienceEngineering
IEEE Transactions on Pattern Analysis and Machine IntelligenceEigenfaces vs. Fisherfaces: recognition using class specific linear projection
11,753 Citations1997Peter N. Belhumeur, João P. Hespanha +1 more
Neural ComputationNonlinear Component Analysis as a Kernel Eigenvalue Problem
8,078 Citations1998Bernhard Schölkopf, Alexander J. Smola +1 more
A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Neural ComputationLaplacian Eigenmaps for Dimensionality Reduction and Data Representation
7,709 Citations2003Mikhail Belkin, Partha Niyogi
This work proposes a geometrically motivated algorithm for representing the high-dimensional data that provides a computationally efficient approach to nonlinear dimensionality reduction that has locality-preserving properties and a natural connection to clustering.
IEEE Transactions on Pattern Analysis and Machine IntelligenceStatistical pattern recognition: a review
6,688 Citations2000Anil K. Jain, Peter Duin +1 more
The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Knowledge and Information SystemsTop 10 algorithms in data mining
5,685 Citations2007Xindong Wu, Vipin Kumar +12 more
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Face recognition using eigenfaces
5,211 Citations2002Matthew Turk, Alex Pentland
An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described.
Journal of Machine Learning ResearchDistance Metric Learning for Large Margin Nearest Neighbor Classification
4,253 Citations2009Kilian Q. Weinberger, Lawrence K. Saul
This paper shows how to learn a Mahalanobis distance metric for kNN classification from labeled examples in a globally integrated manner and finds that metrics trained in this way lead to significant improvements in kNN Classification.
Locality Preserving Projections
3,955 Citations2003Xiaofei He, Partha Niyogi
These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold.
IEEE Transactions on Pattern Analysis and Machine IntelligenceTwo-dimensional pca: a new approach to appearance-based face representation and recognition
3,564 Citations2004Jian Yang, David Zhang +2 more
A new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation that is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction.
IEEE Transactions on Pattern Analysis and Machine IntelligenceFace recognition using Laplacianfaces
3,276 Citations2005Xiaofei He, Shuicheng Yan +3 more
Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
IEEE Transactions on Pattern Analysis and Machine IntelligencePCA versus LDA
3,213 Citations2001Aleix M. Martı́nez, A.C. Kak
The overall conclusion is that when the training data set is small, PCA can outperform LDA and, also, that PCA is less sensitive to different training data sets.
IEEE Transactions on Pattern Analysis and Machine IntelligenceGraph Embedding and Extensions: A General Framework for Dimensionality Reduction
2,863 Citations2006Shuicheng Yan, Dong Xu +4 more
Neighbourhood Components Analysis
1,729 Citations2004Jacob Goldberger, Geoffrey E. Hinton +2 more
A novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm that directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.
Neural ComputationGeneralized Discriminant Analysis Using a Kernel Approach
1,679 Citations2000G. Baudat, F. Anouar
A new method that is close to the support vector machines insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space to deal with nonlinear discriminant analysis using kernel function operator.
IEEE Transactions on Neural NetworksMPCA: Multilinear Principal Component Analysis of Tensor Objects
855 Citations2008Haiping Lu, Konstantinos N. Plataniotis +1 more
It is shown that even without a fully optimized design, an MPCA-based gait recognition module achieves highly competitive performance and compares favorably to the state-of-the-art gait recognizers.
IEEE Transactions on Neural NetworksEfficient and Robust Feature Extraction by Maximum Margin Criterion
845 Citations2006Haizhou Li, Tao Jiang +1 more
This paper proposes some new feature extractors based on maximum margin criterion (MMC) and establishes a new linear feature extractor that does not suffer from the small sample size problem, which is known to cause serious stability problems for LDA.
IEEE Transactions on Pattern Analysis and Machine IntelligenceKPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition
831 Citations2005Jian Yang, Alejandro F. Frangi +3 more
A two-phase KFD framework is developed, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA), which provides novel insights into the nature of KFD.
Pattern RecognitionSparsity preserving projections with applications to face recognition
811 Citations2009Lishan Qiao, Songcan Chen +1 more
A new unsupervised DR method called sparsity preserving projections (SPP), which aims to preserve the sparse reconstructive relationship of the data, which is achieved by minimizing a L1 regularization-related objective function.
IEEE Transactions on Image ProcessingOrthogonal Laplacianfaces for Face Recognition
793 Citations2006Deng Cai, Xiaofei He +2 more
An appearance-based face recognition method, called orthogonal Laplacianface, based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold.
Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods
708 Citations2002Ming–Hsuan Yang
Experimental results show that kernel methods provide better representations and achieve lower error rates for face recognition, which are compared with classical algorithms such as Eigenface, Fisherface, ICA, and Support Vector Machine.
Neural Information Processing SystemsTwo-Dimensional Linear Discriminant Analysis
639 Citations2004Jieping Ye, Ravi Janardan +1 more
2DLDA, a novel LDA algorithm, which stands for 2-Dimensional Linear Discriminant Analysis, overcomes the singularity problem implicitly, while achieving efficiency and the combination of 2DLDA and classical LDA, namely 2 DLDA+LDA, is studied.
Local Discriminant Embedding and Its Variants
563 Citations2005Hwann-Tzong Chen, Huang-Wei Chang +1 more
A new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification, in which the neighbor and class relations of data are used to construct the embedding for classification problems.
IEEE Transactions on Pattern Analysis and Machine IntelligenceGlobally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics
500 Citations2007Jian Yang, David Zhang +2 more
The experimental results show that UDP consistently outperform LPP and PCA and outperforms LDA when the training sample size per class is small, demonstrating that UDP is a good choice for real-world biometrics applications.
PubMedGraph embedding and extensions: a general framework for dimensionality reduction.
381 Citations2007Shuicheng Yan, Dong Xu +4 more
A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability.
Tensor Subspace Analysis
350 Citations2005Xiaofei He, Deng Cai +1 more
A new algorithm called Tensor Subspace Analysis (TSA) is proposed that detects the intrinsic local geometrical structure of the tensor space by learning a lower dimensional tensor subspace and achieves better recognition rate, while being much more efficient.
IEEE Transactions on Image ProcessingMultilinear Discriminant Analysis for Face Recognition
342 Citations2006Shuicheng Yan, Dong Xu +4 more
This paper presents a novel approach to solve the supervised dimensionality reduction problem by encoding an image object as a general tensor of second or even higher order, and proposes a discriminant tensor criterion, whereby multiple interrelated lower dimensional discriminative subspaces are derived for feature extraction.
IEEE Transactions on Pattern Analysis and Machine IntelligenceOrthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique
314 Citations2007Effrosyni Kokiopoulou, Yousef Saad
This paper proposes a method, named orthogonal neighborhood preserving projections, which works by first building an "affinity" graph for the data in a way that is similar to the method of locally linear embedding (LLE); in contrast with the standard LLE, ONPP employs an explicit linear mapping between the input and the reduced spaces.
Pattern RecognitionTwo-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition
276 Citations2006Dewen Hu, Guiyu Feng +1 more
A novel algorithm for image feature extraction, namely, the two-dimensional locality preserving projections (2DLPP), which directly extracts the proper features from image matrices based on locality preserving criterion is proposed.
Image and Vision ComputingFace recognition using discriminant locality preserving projections
275 Citations2006Weiwei Yu, Xiaolong Teng +1 more
The improvement of DLPP algorithm over LPP method benefits mostly from two aspects: one aspect is that DLPP reduces the energy of noise and transformation difference as much as possible without sacrificing much of intrinsic difference.
Pattern RecognitionDiscriminant sparse neighborhood preserving embedding for face recognition
249 Citations2012Jie Gui, Zhenan Sun +4 more
DSNPE not only preserves the sparse reconstructive relationship of SNPE, but also sufficiently utilizes the global discriminant structures from the following two aspects: maximum margin criterion (MMC) is added into the objective function of DSNPE.
Pattern RecognitionEnsemble of local and global information for finger–knuckle-print recognition
206 Citations2010Lin Zhang, Lin Zhang +4 more
The experimental results demonstrate that the proposed local-global information combination scheme could significantly improve the recognition accuracy obtained by either local or global information and lead to promising performance of an FKP-based personal authentication system.
IEEE Transactions on Neural Networks and Learning SystemsSparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition
200 Citations2013Jian Yang, Delin Chu +3 more
A dimensionality reduction method that fits SRC well, which maximizes the ratio of between- class reconstruction residual to within-class reconstruction residual in the projected space and thus enables SRC to achieve better performance.
Pattern RecognitionComment on “two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition”
198 Citations2007Dewen Hu, Guiyu Feng +1 more
Pattern RecognitionLPP solution schemes for use with face recognition
177 Citations2010Yong Xu, Aini Zhong +2 more
Experimental results show that the proposed LPP solution scheme is able to classify much more accurately than conventional LPP and to obtain a classification performance that is only little influenced by the definition of neighbor samples.
Pattern RecognitionSupervised optimal locality preserving projection
122 Citations2011Wai Keung Wong, Hanbing Zhao
This paper proposes two feature extraction algorithms, supervised optimal locality preserving projection (SOLPP) and normalized Laplacian-based supervised optimal localserving projection (NL-SOL PP), and shows that the proposed SOLPP and NL-SolPP achieve much higher recognition accuracy.
Information SciencesKernel class-wise locality preserving projection
108 Citations2007Junbao Li, Jeng‐Shyang Pan +1 more
This paper proposes a novel local structure based feature extraction method, called class-wise locality preserving projection (CLPP), which utilizes class information to guide the procedure of feature extraction.
Pattern RecognitionFace recognition using discriminant locality preserving projections based on maximum margin criterion
102 Citations2010Gui-Fu Lu, Zhong Lin +1 more
Experiments show the effectiveness of the proposed DLPP/MMC, a new discriminant locality preserving projections based on maximum margin criterion, on the ORL, Yale, FERET, and PIE face databases.
NeurocomputingLocality preserving discriminant projections for face and palmprint recognition
100 Citations2010Jie Gui, Wei Jia +3 more
The proposed LPDP combining manifold criterion and Fisher criterion has more discriminanting power, and is more suitable for recognition tasks than LPP, which considers only the local information for classification tasks.
Pattern RecognitionLocal structure based supervised feature extraction
92 Citations2006Haitao Zhao, Shaoyuan Sun +2 more
This paper proposes a novel feature extraction method, called locally discriminating projection (LDP), and compares the proposed LDP approach with LPP, as well as other feature extraction methods, such as PCA and LDA, on the public available data sets, FERET and AR.
IEEE Transactions on Neural NetworksLocality-Preserved Maximum Information Projection
83 Citations2008Haixian Wang, Sibao Chen +2 more
Theoretical analysis shows that conventional linear projection methods such as (weighted) PCA, maximum margin criterion (MMC), linear discriminant analysis (LDA), and LPP could be derived from the LPMIP framework by setting different graph models and constraints.
NeurocomputingLocality and similarity preserving embedding for feature selection
70 Citations2013Xiaozhao Fang, Yong Xu +4 more
A novel unsupervised FS method, i.e., locality and similarity preserving embedding (LSPE) for feature selection, where the nearest neighbor graph is firstly constructed to preserve the locality structure of data points, and then this locality structure is mapped to the reconstruction coefficients such that the similarity among these data points is preserved.
NeurocomputingAn alternative formulation of kernel LPP with application to image recognition
63 Citations2006Guiyu Feng, Dewen Hu +2 more
The proposed kernel locality preserving projections (KLPP) algorithm consists of two steps: kernel principal component analysis (KPCA) plus LPP, and provides an outline for implementing KLPP.
IEEE Transactions on Pattern Analysis and Machine IntelligenceComments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics"
60 Citations2008Weihong Deng, Jiani Hu +3 more
This communication shows that the basic ideas of UDP and LPP are identical, and UDP is just a simplified version of LPP on the assumption that the local density is uniform.
Knowledge-Based SystemsFace recognition using discriminant sparsity neighborhood preserving embedding
60 Citations2012Gui-Fu Lu, Zhong Jin +1 more
An effective supervised dimensionality reduction technique, namely discriminant sparsity neighborhood preserving embedding (DSNPE), for face recognition by taking the advantage of the maximum margin criterion (MMC), which boosts the discriminating power of DSNPE.
NeurocomputingNull space discriminant locality preserving projections for face recognition
58 Citations2008Liping Yang, Weiguo Gong +3 more
A null space discriminant locality preserving projections (NDLPP) method for facial feature extraction and recognition that encodes both the geometrical and discriminant structure of the data manifold, and addresses the small sample size problem by solving an eigenvalue problem in null space.
The Computer JournalLocality-Based Discriminant Neighborhood Embedding
27 Citations2012Jianping Gou, Yi Zhang
Experimental results show that the linear supervised subspace learning method called LDNE can be an effective and robust method for classification and compare it with the state-of-the-art dimensionality reduction techniques such as LPP and DNE on publicly available datasets.
NeurocomputingOptimal locality preserving projection for face recognition
15 Citations2011Yu Chen, Xiaohong Xu +1 more
A novel algorithm called Optimal Locality Preserving Projections (O-LPP) is proposed, which transforms the singular eigensystem computation to eigenvalue decomposition problems without losing any discriminative information, which can reduce the computation complexity.
Neural Computing and ApplicationsFast Fisher Sparsity Preserving Projections
14 Citations2012Fei Yin, Licheng Jiao +3 more
This paper proposes two novel efficient dimensionality reduction methods named Fast Sparsity Preserving Projections (FSPP) and Fast Fisher Sparsity preserves (FFSPP), respectively, which aim to preserve the sparse representation structure in high-dimensional data.
Neural Processing LettersFeature Extraction Based on Maximum Nearest Subspace Margin Criterion
11 Citations2012Yi Chen, Zhenzhen Li +1 more
The maximum nearest subspace margin criterion for feature extraction is proposed, which significantly improves the performances of SRC and LRC and extends LRC to overcome the potential singular problem.
Optical EngineeringNeighborhood discriminant embedding in face recognition
7 Citations2010Dexing Zhong
A novel feature extraction method for face recognition called neighborhood discriminant embedding NDE is presented, which incor- porates graph embedding and Fisher's criterion and includes an indi- vidual discriminative factor IDF.
Lecture notes in computer scienceA Unified Framework of Subspace and Distance Metric Learning for Face Recognition
6 Citations2007Qingshan Liu, Dimitris Metaxas
This paper proposes a unified scheme of subspace and distance metric learning under the Bayesian framework for face recognition, and formulate it by minimizing the Bhattachyrra coefficient between two distributions.
Neural Processing LettersSupervised Discriminant Projection with Its Application to Face Recognition
4 Citations2011Wang Jianguo, Jizhao Hua
A novel subspace method, called supervised discriminant projection (SDP), is proposed for face recognition, which takes special consideration of both the local information and class information in the procedure of feature extraction.