Geometric Mean for Subspace Selection

By Dacheng Tao, Xuelong Li, Xindong Wu, S. Maybank. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009

590 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3bf2955b6c88b83f13badc33f30d261a7ff53c04

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Citations in the corpus

Constrained Nonnegative Matrix Factorization for Image Representation (2012, background): “A fundamental problem in many data analysis tasks is to find a suitable representation of the data [1], [2], [3], [4], [5], [6], [7], [8].”
NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization (2012, background): “Since NeNMF- does not divide into two parts as in (37), it can be adopted to wider range of criterions such as geometric mean [8] and patch alignment framework [28], [30], [32].”
Cross-Modality Submodular Dictionary Learning for Information Retrieval (2014, unknown): “While classical approaches [ 34 ] , [ 39 ] , [ 41 ] , [ 20 ] , [ 40 ] , which only utilize a unimodal representation, e.g., text, image, audio, etc., are considered somewhat outdated, multimodal systems [ 37 ] , [ 9 ] , [ 44 ] become popular for information retrieval.”
Learning Discriminant Face Descriptor (2014, background): “Recently, many advanced subspace analysis methods [15], [16] that address the problem of small sample size (SSS) are proposed to enhance the discriminative ability of the learned subspace.”
Multi-view hypergraph learning by patch alignment framework (2013, methodology): “It is proposed as a powerful analysis and development tool for dimensionality reduction.”
Distribution Calibration in Riemannian Symmetric Space (2011, background): “Practically, sufficient applications have shown the effectiveness of distribution calibration algorithms for cross-domain learning.”
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent (2011, methodology): “In addition, discriminative dimension reduction has been popularly used in pattern classification [ 39 ], [40], [10].”
Bregman Divergence-Based Regularization for Transfer Subspace Learning (2010, background): “C ONVENTIONAL algorithms [2], [4], [15] for image and video data analysis and organization perform well under the assumption that training and testing samples are independent and identically distributed (i.i.d.).”
Local structure preserving discriminative projections for RGB-D sensor-based scene classification (2015, methodology): “In this paper, we compare LSPDP with five representative subspace methods: PCA [19], LDA [14], geometric mean-based subspace selection (GMSS) [44], discriminative locality alignment (DLA) [59], and supervised locality preserving projections (SLPP) [17].”
A Comprehensive Survey on Pose-Invariant Face Recognition (2015, methodology): “The pose-varied face images are distributed on a highly nonlinear manifold Tenenbaum et al. (2000), which greatly degrades the performance of traditional face recognition models that are based on the single linear subspace assumption Turk and Pentland (1991).”