Learning a Mahalanobis distance metric for data clustering and classification

By Shiming Xiang, Feiping Nie, Changshui Zhang. Pattern Recognition, 2008

630 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1aa90b3a1b1b757eabdab5207239f9c37029d85f

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Liang Lin
2. Luc Van Gool
3. Xiaogang Wang
4. Yi Yang
5. Dacheng Tao
6. Qi Tian
7. Ming-Hsuan Yang
8. Andrea Vedaldi

Citations in the corpus

• Trace optimization and eigenproblems in dimension reduction methods (2011, background): “The original version shown above has resurfaced in recent years, see, e.g., [19, 49, 56, 59] among others.”
• ClusterGAN : Latent Space Clustering in Generative Adversarial Networks (2018, background): “Clustering has been extensively studied in unsupervised learning with multifarious approaches seeking efficient algorithms [24], problem specific distance metrics [29], validation [12] and the like.”
• Semi-supervised Kernel Metric Learning Using Relative Comparisons (2016, result): “Xiang et al. [2008] use metric learning, as well, to implement ML/CL constraints in a clustering and classification framework, while Kumar and Kummamuru [2008] follow a similar approach using relative comparisons.”
• Metric learning for generalizing spatial relations to new objects (2017, methodology): “applied to address face recognition [7], image classification [9] and image segmentation [26].”
• Multiple criteria construction management decisions considering relations between criteria (2010, methodology): “Mahalanobis distance metric is a proper method for data clustering and classification, pattern recognition (Xiang et al. 2008).”
• Unsupervised Deep Embedding for Clustering Analysis (2015, background): “…machine learning in terms of feature selection (Boutsidis et al., 2009; Liu & Yu, 2005; Alelyani et al., 2013), distance functions (Xing et al., 2002; Xiang et al., 2008), grouping methods (Mac-Queen et al., 1967; Von Luxburg, 2007; Li et al., 2004), and cluster validation (Halkidi et al., 2001).”
• Deep feature learning with relative distance comparison for person re-identification (2015, background): “There are also a number of general distance learning methods that have been rarely exploited in the context of person re-identification problems [15, 16, 17, 18].”