Subclass discriminant analysis

By Manli Zhu, Aleix M. Martinez. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

383 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e13b459ba99a34d8ef06fc4bbbf24570a802eedf

How researchers see this paper

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

In Artificial Intelligence

1. Anton van den Hengel
2. Chunhua Shen
3. Anthony R. Dick
4. Xilin Chen
5. Ruiping Wang
6. Mehrtash Harandi
7. Gilson Antonio Giraldi
8. Carlos Eduardo Thomaz

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Chunhua Shen
4. Xilin Chen
5. Xiaogang Wang
6. Ming-Hsuan Yang
7. Anton van den Hengel
8. Andrew Zisserman

In Statistics and Probability

1. Xilin Chen
2. Gilson Antonio Giraldi
3. Ruiping Wang
4. Carlos Eduardo Thomaz
5. Carlos Fabian Benitez-Quiroz
6. Shaeela Ayesha
7. Mehrtash Harandi
8. Babak Nadjar Araabi

Citations in the corpus

• A new ranking method for principal components analysis and its application to face image analysis (2010, methodology): “Zhu and Martinez’s approach is restricted to the LDA spectral solution but it has been successfully employed to solve the small sample size problems in discriminant analysis, outperforming consistently a number of discriminant analysis methods using several different datasets [10,19,20].”
• EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild (2016, methodology): “Hence, our KSDA-based classifier is given by the solution to, To do this, we first divide the training set D i into five subclasses .”
• Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition (2009, methodology): “Method G3/P23 G7/P19 G11/P15 Eigenface [1] 63.66, σ=7.1 74.7, σ=5.1 77.15, σ=2.8 Fisherface [2] 75.75, σ=5.6 86.0, σ=1.1 87.70, σ=3.0 SDA on Eigenface [3] 75.18, σ=5.3 83.79, σ=3.3 87.13, σ=1.5 Random subspace LDA [11] 77.72, σ=6.4 89.74, σ=1.9 91.04, σ=0.8 Proposed Method 82.47, σ=5.2 90.33, σ=1.8 ”
• Overview and comparative study of dimensionality reduction techniques for high dimensional data (2020, positive): “To address these issues, Subclass Discriminant Analysis (SDA) [148] and Mixture Subclass DA (MSDA) [149] has been developed.”
• Manifold Discriminant Analysis (2009, methodology): “To improve LDA, many extensions have been developed, such as Nonparametric Discriminant Analysis (NDA) [5], Subclass Discriminant Analysis (SDA) [33], Maximum Margin Criterion (MMC) [18], and Kernel Fisher Discriminant (KFD) [19].”
• Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces (2020, methodology): “The concept of “sub-class” applied in face recognition was first introduced in [42,43], where a mixture of Gaussians was used to approximate the underlying distribution of each class.”
• A Weakly Supervised Fine Label Classifier Enhanced by Coarse Supervision (2019, background): “Along with these developments, image sub-categorization has been used to increase performance of a wide variety of applications in computer vision, such as face recognition [39] and object detection [10].”
• A survey of appearance models in visual object tracking (2013, positive): “For example, Xu et al. [2008] take advantage of adaptive subclass discriminant analysis (SDA) (i.e., an extension to the basic SDA [Zhu and Martinez 2006]) for object tracking.”