- Norm Regularized Discriminative Feature Selection for Unsupervised Learning

By Yi Yang, Heng Tao Shen, Zhigang Ma, Zi-Liang Huang, Xiaofang Zhou. 2011

How researchers see this paper

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

1. Dacheng Tao
2. Mingsheng Long
3. Yi Yang
4. Kai Han
5. Yuxin Peng
6. Jianmin Wang
7. Xuelong Li
8. Yunhe Wang

Citations in the corpus

• Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking (2018, unknown): “The closed-form optimal solution can directly be calculated as [71]:”
• Unsupervised Feature Selection Using Nonnegative Spectral Analysis (2012, methodology): “Finally, UDFS and NDFS achieve higher ACC and NMI by evaluating features jointly than others that select features one after another or using two-step strategies.”
• Transfer Joint Matching for Unsupervised Domain Adaptation (2014, methodology): “Our proof is similar to methods in [18, 27].”
• Unsupervised Feature Selection with Structured Graph Optimization (2016, methodology): “To validate the effectiveness of SOGFS, we compare it with several state-of-the-art unsupervised feature selection approaches, including Laplacian Score (LS) (He, Cai, and Niyogi 2005), Multi Cluster Feature Selection (MCFS) (Cai, Zhang, and He 2010), Unsupervised Discriminate Feature Selection (UDF”
• Features in Concert: Discriminative Feature Selection meets Unsupervised Clustering (2014, methodology): “ation, even from limited training data. We are not the first to see a connection between clustering and feature selection. Some consider the inverse task: feature selection for unsupervised clustering [25,48]. Others propose efficient selection of features through diversity [45]. However, we are the fi”
• Feature Selection (2016, methodology): “Some representative criteria include feature discriminative ability to separate samples [Kira and Rendell 1992; Robnik-Šikonja and Kononenko 2003; Yang et al. 2011; Du et al. 2013; Tang et al. 2014b], feature correlation [Koller and Sahami 1995; Guyon and Elisseeff 2003], mutual information [Yu and ”
• Robust Structured Subspace Learning for Data Representation (2015, background): “, Accuracy (ACC) and normalized mutual information (NMI) [18], [19].”
• Learning Cross-Media Joint Representation With Sparse and Semisupervised Regularization (2014, background): “Recent works have shown the effectiveness of applying l2,1-norm based models to single media feature selection [32], [33].”
• Reconstruction-based Unsupervised Feature Selection: An Embedded Approach (2017, methodology): “…labeled data is particular expensive requiring both time and effort, unsupervised feature selection has received increasingly attention in the past few years [He et al., 2005; Cai et al., 2010; Yang et al., 2011; Li et al., 2012; Qian and Zhai, 2013; Li et al., 2015; 2016b; Wei and Philip, 2016].”
• Pattern Recognition (2018, unknown): “As pointed out by Wu et al. [5] , spatial data augmentation including random cropping, random scale and random rotation does not improve performance.”
• Learning Instance-wise Sparsity for Accelerating Deep Models (2019, methodology): “For an optimal feature elimination, we propose to utilize the (cid:96) 2 , 1 -norm to learn sparse features of different instances, which is a widely used regularization to select features [ Yang et al. , 2011 ] .”