Semi-supervised Discriminant Analysis

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

744 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6fddcb525f65d76c49b13a75d3f7c33bcb854833

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In Computer Vision and Pattern Recognition

Citations in the corpus

Non-negative low rank and sparse graph for semi-supervised learning (2012, methodology): “We vary the parameters and evaluate the classification performance of NNLRS-graph based SDA on PIE face database.”
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction (2010, methodology): “We briefly review the prior semi-supervised learning work: local and global consistency (LGC) [36], Gaussian fields and harmonic functions (GFHF) [37], manifold regularization (MR) [4], [20], [21] and semi-supervised discriminant analysis (SDA) [ 6 ].”
Trace optimization and eigenproblems in dimension reduction methods (2011, background): “in [61] proposed the so called Semi-supervised Discriminant Analysis (SDA), which is a regularized variant of LDA that takes into account the unlabeled data.”
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback (2012, positive): “Using the same distance-based ranking method, TRRF-E is better than SDA and SSP.”
Supervised translation-invariant sparse coding (2010, methodology): “As shown, both our unsupervised and supervised sparse coding algo-rithm signiﬁcantly outperform S-LDA [5], reported as state-of-the-art performance algorithm on this database, reducing the error rate by more than 10 times.”
Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, positive): “Moreover, we have provided evidence that the LDS overperforms the semi-supervised discriminant analysis (Cai et al., 2007) in MCI-to-AD conversion prediction in our recent conference paper (Moradi et al., 2014).”
Robust Structured Subspace Learning for Data Representation (2015, methodology): “4) SDA [35].”
Bregman Divergence-Based Regularization for Transfer Subspace Learning (2010, background): “Similar to semisupervised learning [4], [18], [34], although transfer learning uses both labeled and unlabeled samples, transfer learning is intrinsically different from semisupervised learning.”
Enhancing Person Re-identification in a Self-Trained Subspace (2017, methodology): “Different with our approach, [48] is extended from linear discriminant analysis (LDA), while our approach seems more like a semi-supervised locality preserving projections (LPP) [51].”
Learning With $\ell ^{1}$-Graph for Image Analysis (2010, background): “2) Set the graph Laplacian matrix , where is a diagonal matrix with .”
A Unified Feature Selection Framework for Graph Embedding on High Dimensional Data (2015, methodology): “For example, the semi-supervised discriminative analysis (SDA) method [40] builds upon the LDA and LPP graph embeddings.”
Image retrieval via probabilistic hypergraph ranking (2010, background): “Cai et al. put forward semi-supervised discriminant analysis [7] and active subspace learning [6] to relevance feedback based image retrieval.”
Distribution Calibration in Riemannian Symmetric Space (2011, methodology): “To demonstrate the superiority of DC-RSS, we will compare DC-RSS with three classical subspace learning algorithms, including principal component analysis (PCA) [16], Fisher’s linear discriminative analysis (FLDA) [14] and semisupervised discriminate analysis (SDA) [ 6 ].”
Transferred Dimensionality Reduction (2008, methodology): “Recently, the semi-supervised method has also been developed [3], which makes use of both labeled and unlabeled data.”