Hierarchical Classification via Orthogonal Transfer

By Lin Xiao, Dengyong Zhou, Mingrui Wu. 2011

106 citations recorded by the source corpus.

How researchers see this paper

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

1. Xu Han
2. Qiang Yang
3. Yoshua Bengio
4. Zhiyuan Liu
5. Dacheng Tao
6. Yu Zhang
7. Mohit Bansal
8. Ruslan Salakhutdinov

Citations in the corpus

• Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN (2018, background): “The idea of transfer learning can also be borrowed to improve each of the classifiers in the hierarchy [43].”
• Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention (2018, methodology): “Hierarchical information is widely applied for model enhancement, especially for classification models (McCallum et al., 1998; Rousu et al., 2005; Weinberger and Chapelle, 2009; Zhao et al., 2011; Bi and Kwok, 2011; Zhou et al., 2011; Verma et al., 2012).”
• Constrained Semi-supervised Learning in the Presence of Unanticipated Classes (2016, background): “There has been a lot of research done in the area of supervised hierarchical classification [21, 55, 142].”
• Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies (2016, background): “There has been a lot of research done in the area of supervised hierarchical classification [6, 16, 24].”
• Combined Group and Exclusive Sparsity for Deep Neural Networks (2017, methodology): “Zhou et al. (2011) enforced orthogonality via explicit dot product regularization to make the parameters for parent-level and childlevel classifiers in a hierarchical classifier to be orthogonal.”
• Marginalized Denoising Auto-encoders for Nonlinear Representations (2014, background): “It is worth pointing out that this type of orthogonality regularization has been used in other settings of learning models with disjoint sets of features (Hwang et al., 2011; Zhou et al., 2011; Chen et al., 2011).”
• Hierarchical Regularization Cascade for Joint Learning (2013, background): “, 2012), induce orthogonal transfer down the hierarchy (Zhou et al., 2011) or detect novel classes (Weinshall et al.”