Spectral Regression for Efficient Regularized Subspace Learning

By Deng Cai, Xiaofei He, Jiawei Han. IEEE International Conference on Computer Vision, 2007

377 citations recorded by the source corpus.

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Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection (2014, methodology): “We determine it as in traditional approaches, such as [41].”
Ieee Transactions on Knowledge and Data Engineering Context-aware Hypergraph Construction for Robust Spectral Clustering (neutral): “Spectral clustering is an effective means of clustering data with complex topological structure [8]–[13], [15], [16], [31], [34]–[36].”
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction (2010, methodology): “TOP-1 RECOGNITION PERFORMANCE (MEAN RECOGNITION ACCURACY STANDARD DEVIATION %) OF PCA [25], LPP [12], LPP-SR [ 7 ], AND FME/U OVER 20”
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.”
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis (2008, background): “Please see [4], [5], [6] for more details.”
Coupled Kernel Embedding for Low-Resolution Face Image Recognition (2012, background): “Compared with highresolution (HR) images, these LR images lose some discriminative details across different subjects.”