Local discriminant embedding and its variants

By Hwann-Tzong Chen, Huang Chang, Tyng-Luh Liu. Computer Vision and Pattern Recognition, 2005

625 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1d25f0490f561ac7edb83b0a28ea7c0dfa08dfe5

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In Computer Vision and Pattern Recognition

Citations in the corpus

Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary (2010, methodology): “Therefore, subspace learning and manifold learning methods have been dominantly and successfully used in appearance based FR [4], [5], [6], [7], [8], [9], [10], [11].”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, background): “For instance, linear discriminant analysis (LDA), local Fisher discriminant analysis (LFDA) [101], local discriminant embedding (LDE) [102] and nonparametric weighted feature extraction (NWFE) [103].”
Discriminant Locally Linear Embedding With High-Order Tensor Data (2008, background): “Dai and Yeung additionally extended neighborhood preserving embedding (NPE) [11], which is the direct linearization of LLE (LLE/L) [29], and also local discriminant embedding (LDE) [5], which is a variant of classical nonparametric LDA [7], to tensor NPE and tensor LDE, respectively.”
Metaface learning for sparse representation based face recognition (2010, methodology): “For a test sample my from this class, intuitively, y could be well approximated by the linear combination of the samples within Ai, i.e. , ,1 in i j i j i ij Ay s , where 1601978-1-4244-7994-8/10/$26.00 ©2010 IEEE ICIP 2010 ,1 ,2 ,[ , ,..., ] ii nT i i i ni are the coefficients.”
Human Age Estimation With Regression on Discriminative Aging Manifold (2008, methodology): “Since the final age estimation comes down to classification, we want to learn a manifold embedding that is sufficiently discriminative [3], [24], [29].”
Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, background): “Most existing manifold learning algorithms [2, 3, 4, 10, 33] empirically select the optimal feature dimension because there is only one projection matrix to be sought for feature extraction.”
Exploiting Known Taxonomies in Learning Overlapping Concepts (2007, methodology): “The typical algorithms includes Locality Preserving Projections (LPP, [He and Niyogi, 2003]), Local Discriminant Embedding (LDE, [Chen et al., 2005]), Marginal Fisher Analysis (MFA, [Yan et al., 2005]), etc. LPP uses one graph to model the geometrical structure in the data.”
Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" (2007, background): “’s [33] and Chen’s [34] ideas) to facilitate this characterization and then to build a semisupervised hybrid system.”
Maximum Neighborhood Margin Discriminant Projection for Classification (2014, background): “These LPP-based DR algorithms can be mainly put into two categories: supervised and unsupervised ones.”
Trace Ratio vs. Ratio Trace for Dimensionality Reduction (2007, methodology): “Many popular dimensionality reduction algorithms, such as Linear Discriminant Analysis (LDA) [1] and the nonparametric algorithm Marginal Fisher Analysis (MFA) [10] (or similarly, Local Discriminant Embedding [2]), can be formulated in the above graph embedding framework.”
Learning a Spatially Smooth Subspace for Face Recognition (2007, background): “(2), the objective function of spatially smooth MFA (LDE) is: which leads to the maximum eigenvalue problem It would be important to note that the 2-”
Semantic manifold learning for image retrieval (2005, methodology): “While the similarity property is used as a constraint enforcing the preservation of the local geometry, we make use of the relevant and irrelevant feedback information in a discriminant manner [5], i.”
Manifold Discriminant Analysis (2009, background): “MDA vs. MFA [28], LDE [7] and KFD [19].”
Deep learning for remote sensing image classification: A survey (2018, methodology): “In Zhao and Du (2016), based on local discriminant embedding (LDE) (Chen, Chang, & Liu, 2005), a balanced LDE method was proposed and jointly used with a 2D-CNN to obtain the final classification result.”
Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach (2016, background): “Commonly, the unified criterion is chosen to maximize the Fisher’s ratio objective function [18] as”
Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimizing Global Loss Functions (2015, neutral): “Even though these methods show substantial gains compared to the previous state of the art in public benchmark datasets [3, 4, 5, 24, 28, 31, 32], we believe that the loss functions and network structures being explored for this task can be improved.”
Neighborhood repulsed metric learning for kinship verification (2012, neutral): “Representative examples include face recognition [5], [8], [10], [20], [22], [27], [28], [35], [37], [40], [41], [42], [43], [54], [55], [56], [58], [64], [65], [66], [67], [68], facial expression recognition [11], [18], [70], facial age estimation [19], [21], [24], [25], [31], [39], gender classifi”
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics (2007, background): “Yet, as it is modeled on the basis of “locality,” LPP, like most manifold learning algorithms, has the weakness of having no direct connection to classification.”