SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis

By Deng Cai, Xiaofei He, Jiawei Han. IEEE Transactions on Knowledge and Data Engineering, 2008

464 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5829b673e11b14fa308e8176dbd616d795a1cdb0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Philip Chen
2. Chunming Zhang
3. Shuiwang Ji
4. Liang Sun
5. Dacheng Tao
6. Si Si
7. Jieping Ye
8. Mohamed Nadif

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Luc Van Gool
3. Ming-Hsuan Yang
4. Yi Yang
5. Andrew Zisserman
6. Xiaogang Wang
7. Shuicheng Yan
8. Bernt Schiele

In Media Technology

1. Luc Van Gool
2. Radu Timofte
3. Xiaofei He
4. Deng Cai
5. Jiawei Han
6. Philip Chen
7. Chunming Zhang
8. Dacheng Tao

Citations in the corpus

• A Semi-NMF-PCA Unified Framework for Data Clustering (2017, methodology): “For, LDA we have used the code provided by Cai et al. [35].”
• Bregman Divergence-Based Regularization for Transfer Subspace Learning (2010, methodology): “To demonstrate the effectiveness of the proposed TSL in text categorization, we compare the proposed TSL examples, e.g., TFLDA, TLPP, TMFA, and TDLA, with conventional subspace learning algorithms, e.g., FLDA, LPP, MFA, DLA, and the SDA.”
• We Can Hear You with Wi-Fi! (2014, methodology): “Therefore instead of using a single classifier, we explore a more advanced machine learning scheme: Spectral Regression Discriminant Analysis (SRDA) [17].”
• Traffic sign recognition — How far are we from the solution? (2013, methodology): “Note that we use regularized LDA [20] (as in [10]).”
• Spectral regression: a unified subspace learning framework for content-based image retrieval (2007, methodology): “PCA projects the data points into a lower dimensional subspace in which the sample variance is maximized while LDA finds projective directions by maximizing the ratio of between-class scatter to within-class scatter.”
• Hypergraph spectral learning for multi-label classification (2008, methodology): “In our implementation, we use the LSQR algorithm proposed in [18], which is an implementation of a conjugate gradient type method for solving sparse least squares problems [5].”
• Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm (2011, methodology): “For this specif c reason, the recently proposed spectral regression (SR) based linear discriminant analysis [49] and its kernel based version (KSR) [50] were utilized in this paper as two possible variations of dimensionality reduction.”
• Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, methodology): “Taking discriminant analysis as another example, researchers try to develop effective algorithm for large-scale discriminant analysis [33,165].”