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Multi-view discriminant analysis with tensor representation and its application to cross-view gait recognition

Yasushi Makihara, Al Mansur, Daigo Muramatsu, Zasim Uddin, Yasushi Yagi
2015 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)  
we therefore encapsulate the multiple view-specific projection matrices in a framework of discriminant analysis with tensor representation, which enables us to overcome the curse of dimensionality dilemma  ...  This paper describes a method of discriminant analysis for cross-view recognition with a relatively small number of training samples.  ...  MULTI-VIEW DISCRIMINANT ANALYSIS WITH TENSOR REPRESENTATION A. Tensor representation Target objects in computer vision are often represented as a second or higher order tensor rather than a vector.  ... 
doi:10.1109/fg.2015.7163131 dblp:conf/fgr/MakiharaMMUY15 fatcat:uqeay3pza5hwhgjlgz3hsb7p7a

Discriminant Analysis with Tensor Representation

Shuicheng Yan, Dong Xu, Qiang Yang, Lei Zhang, Xiaoou Tang, Hong-Jiang Zhang
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)  
We call this algorithm Discriminant Analysis with Tensor Representation (DATER), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes  ...  Then, a novel approach called k-mode Cluster-based Discriminant Analysis is presented to iteratively learn these subspaces by unfolding the tensor along different tensor dimensions.  ...  Analysis with Tensor Representation As aforementioned, Discriminant Tensor Criterion often has no closed-form solutions.  ... 
doi:10.1109/cvpr.2005.131 dblp:conf/cvpr/YanXYZTZ05 fatcat:7hx2blhucjbxve6idomyy52q6e

Face Recognition by Discriminant Analysis with Gabor Tensor Representation [chapter]

Zhen Lei, Rufeng Chu, Ran He, Shengcai Liao, Stan Z. Li
Lecture Notes in Computer Science  
This paper proposes a novel face recognition method based on discriminant analysis with Gabor tensor representation.  ...  In this paper, we propose a 3rd-order Gabor tensor representation derived from a complete response set of Gabor filters across pixel locations and filter types. 2D discriminant analysis is then applied  ...  In this paper, we propose a novel discriminant analysis method with Gabor tensor representation for face recognition.  ... 
doi:10.1007/978-3-540-74549-5_10 fatcat:hzas2o2bvfaj5i7c32rvhaolse

Discriminating Two Types of Noise Sources using Cortical Representation and Dimension Reduction Technique

Shiva Sundaram, Shrikanth Narayanan
2007 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  
To handle large tensor feature sets, we use a generalized discriminant analysis method to reduce the dimension.  ...  A bio-inspired tensor representation of audio that models the processing at the primary auditory cortex is used for feature extraction.  ...  To reduce the dimension of the extracted data, a generalized version of the discriminant analysis for multidimensional tensor representation was used.  ... 
doi:10.1109/icassp.2007.366654 dblp:conf/icassp/SundaramN07 fatcat:n5nl2qlnhrhzzlqtuiw4amysom

Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction

Jinliang An, Yuzhen Song, Yuwei Guo, Xiaoxiao Ma, Xiangrong Zhang
2019 Remote Sensing  
To achieve this, a tensor discriminant analysis model via compact feature representation (TDA-CFR) was proposed in this paper.  ...  In TDA-CFR, the traditional linear discriminant analysis was extended to tensor space to make the resulting feature representation more informative and discriminative.  ...  In addition, some tensor based discriminant analysis methods extend the discriminant analysis model into tensor space to make the processed dataset more discriminantive, but these methods are directly  ... 
doi:10.3390/rs11151822 fatcat:feqeelkpqndq5if3g6ga2iynmy

Face Recognition by Discriminative Orthogonal Rank-one Tensor Decomposition [chapter]

Gang Hua
2008 Recent Advances in Face Recognition  
With the tensor representation, multi-linear (e.g., bilinear for order 2 tensors) are pursued for discriminative subspace analysis.  ...  Why it is better to perform discriminative subspace analysis on this Global-Local tensor representation?  ...  The chapters presented use innovative approaches to deal with a wide variety of unsolved issues.  ... 
doi:10.5772/6392 fatcat:sacnydntmfe5jacvkugtaghqcu

Spatial‐spectral feature extraction of hyperspectral images using tensor‐based collaborative graph analysis

Lei Pan
2021 Electronics Letters  
To fully exploit the 3D spatial-spectral structural information, the collaborative representation model is extended to tensor space by using the third-order tensor representation of HSI, in which samples  ...  To address these problems, a novel spatial-spectral feature extraction method, i.e. tensor-based collaborative graph analysis, is proposed in this letter.  ...  (TLPP) [16] , tensor discriminative locality alignment (TDLA) [17] , and general tensor discriminative analysis (GTDA) [18] .  ... 
doi:10.1049/ell2.12109 fatcat:nfikfiaxufa2hk4hqxx5ndfj2u

Empirical Discriminative Tensor Analysis for Crime Forecasting [chapter]

Yang Mu, Wei Ding, Melissa Morabito, Dacheng Tao
2011 Lecture Notes in Computer Science  
Using the tensor data structure, we propose the Empirical Discriminative Tensor Analysis (EDTA) algorithm to obtain sufficient discriminative information while minimizing empirical risk simultaneously.  ...  In this paper, we discuss a new four-order tensor representation for crime data. The tensor encodes the longitude, latitude, time, and other relevant incidents.  ...  Sun et al. proposes the Dynamic Tensor Analysis (DTA) and Streaming Tensor Analysis (STA) methods to cope with the increasing data number problem.  ... 
doi:10.1007/978-3-642-25975-3_26 fatcat:76hy2s32zjcjjj6ty7xjk35aee

Multilinear Discriminant Analysis for Face Recognition

Shuicheng Yan, Dong Xu, Qiang Yang, Lei Zhang, Xiaoou Tang, Hong-Jiang Zhang
2007 IEEE Transactions on Image Processing  
Index Terms-2-D LDA, 2-D PCA, linear discriminant analysis (LDA), multilinear algebra, principal component analysis (PCA), subspace learning.  ...  First, we propose a discriminant tensor criterion, whereby multiple interrelated lower dimensional discriminative subspaces are derived for feature extraction.  ...  Classification With Multilinear Discriminant Analysis With the learned projection matrices , the low-dimensional representation of the training sample , , can be computed as .  ... 
doi:10.1109/tip.2006.884929 pmid:17283779 fatcat:p22kpl5fgrfsvosz6ex2mnawc4

General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank
2007 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA.  ...  The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality  ...  Index Terms-Gabor Gait, General Tensor Discriminant Analysis, Human Gait Recognition, Linear Discriminant Analysis, Tensor Rank, Visual Surveillance. function representations are introduced.  ... 
doi:10.1109/tpami.2007.1096 pmid:17699917 fatcat:y5m3ymzqsngtzjg5nq5rost42y

Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery

Chengcheng Jia, Guoqiang Zhong, Yun Fu
Experiments demonstrate that our method results with better accuracy compared with some other state-of-the-art low-rank tensor representation learning approaches on the MSR Hand Gesture 3D database and  ...  In order to integrate useful supervisory information for classification, we adopt a discriminant analysis criterion to learn the projection matrices.  ...  Meanwhile, with this discriminant analysis criterion, supervisory information is seamlessly integrated in the low-rank tensor completion model.  ... 
doi:10.1609/aaai.v28i1.8901 fatcat:zxjvsvhwcrdzvlbj7et37dldx4

Multilinear class-specific discriminant analysis

Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
2017 Pattern Recognition Letters  
Many existing works focus on maximizing the inter-class variances to intra-class variances defined on tensor data representations.  ...  There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques.  ...  Multilinear Discriminant Analysis Several works have extended multi-class discriminant analysis criterion in order to utilize the natural tensor representation of the input data ( [21] , [22] , [19]  ... 
doi:10.1016/j.patrec.2017.10.027 fatcat:ghbivb6vore65hjanxo3cgl23u

Face Recognition Using MPCA-EMFDA Based Features Under Illumination and Expression Variations in Effect of Different Classifiers [chapter]

Chandan Tripathi, Aditya Kumar, Punit Mittal
2014 Advances in Intelligent Systems and Computing  
Initially the face datasets have been mapped into curvilinear tensor space and features have been extracted using Multilinear Principal Component Analysis (MPCA) followed by Fisher Discriminant Analysis  ...  The paper proposes a new method for feature extraction using tensor based Each Mode Fisher Discriminant Analysis(EMFDA) over Multilinear Principle Components (MPCA) in effect of different classifiers while  ...  Keeping above views [5, 16] a new adaptive method, named as Multidimensional Discriminant Analysis (MLDA), has been proposed with optimal reduction in feature dimension without compromising with both,  ... 
doi:10.1007/978-3-319-04960-1_5 fatcat:5isb432o4rgrfdwhci5bbihqdu

Ranking Methods for Tensor Components Analysis and Their Application to Face Images

Tiene A. Filisbino, Gilson A. Giraldi, Carlos Eduardo Thomaz
2013 2013 XXVI Conference on Graphics, Patterns and Images  
of the database; (b) Computing discriminant weights through separating hyperplanes, to select the most discriminant CSA tensor components.  ...  Our experimental results highlight the low dimensional data representation of both approaches, while allowing robust discriminant reconstruction and interpretation of the sample groups and high recognition  ...  discriminant analysis (TDA) [7] , [8] and tensor rank-one decomposition [9] , among others.  ... 
doi:10.1109/sibgrapi.2013.50 dblp:conf/sibgrapi/FilisbinoGT13 fatcat:xwsxrjzd2jckjk7aslvzm57oxe

Tensor Discriminant Analysis for View-based Object Recognition

Yong Wang, Shaogang Gong
2006 18th International Conference on Pattern Recognition (ICPR'06)  
Specifically, our experimental results on ETH-80 show the particular strength of this tensor discriminant analysis method when only a small number of training samples with big intra-class variation are  ...  In this paper, we use a general M th order tensor discriminant analysis approach [11] for view based object recognition.  ...  It can also result in a very large image representational space with poor numerical properties and computational tractability. In this work, we use a tensor discriminant analysis approach by Tao et al  ... 
doi:10.1109/icpr.2006.1106 dblp:conf/icpr/WangG06 fatcat:66rgsluwirekhdlscpbpmsjhwu
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