Transferred Dimensionality Reduction

By Zheng Wang, Yangqiu Song, Changshui Zhang. 2008

126 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Pre-Trained Models: Past, Present and Future (2021, background): “, 2008) 2, and unsupervised transfer learning (Wang et al., 2008).”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, methodology): “n exploiting the (unlabeled) information in the source domain to solve unsupervised learning task in the target domain. These tasks include clustering, dimensionality reduction and density estimation [38, 39]. According to this classiﬁcation, DA methods are transductive TL solutions, where it is ass”
Feature-based transfer learning with real-world applications (2010, methodology): “[197] proposed a transferred discriminative analysis (TDA) algorithm to solve the transfer dimensionality reduction problem.”
Generalized Transfer Subspace Learning Through Low-Rank Constraint (2014, background): “More and more research effort is being devoted to visual domain adaptation (Yang et al. 2007; Jiang et al. 2008; Wang et al. 2008; Duan et al. 2009, 2012a,b,c; Si et al. 2010; Saenko et al. 2010; Gopalan et al. 2011; Kulis et al. 2011; Aytar and Zisserman 2011; Qi et al. 2011; Lim et al. 2011; Gong…”
Transfer learning: a friendly introduction (2022, background): “Unsupervised TL, on the other hand, focuses more on completing unsupervised tasks, such as clustering and dimension reduction [13, 14].”
Transfer Learning for Activity Recognition: A Survey (2013, background): “Outside the domain of activity recognition, researchers have leveraged the unlabeled source data to improve transfer in the target domain [16,53,71], but such techniques have yet to be applied to activity recognition.”
A Decade Survey of Transfer Learning (2010–2020) (2020, methodology): “1) Learning on Features: Firstly, a few methods [54], [55] for clustering and dimensional reduction problems were summarized by [2].”
On handling negative transfer and imbalanced distributions in multiple source transfer learning (2014, background): “Unsupervised TL [34,35] concerns unsupervised learning in the target domain in which source domain’s information can be used to help the clustering task.”
A Survey on Transfer Learning (2010, methodology): “TDA first applies clustering methods to generate pseudoclass labels for the target unlabeled data.”
Multi-Task Learning in Heterogeneous Feature Spaces (2011, methodology): “There exist some methods for transfer dimensionality reduction, e.g., (Wang, Song, and Zhang 2008), which utilize information in the source tasks to help dimensionality reduction in the target task, but they cannot handle heterogeneous feature spaces and they, like translated learning, only improve…”