Self-taught clustering

By Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu. 2008

244 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Pre-Trained Models: Past, Present and Future (2021, background): “…(Lawrence and Platt, 2004; Mihalkova et al., 2007; Evgeniou and Pontil, 2004), transductive transfer learning (Shimodaira, 2000; Zadrozny, 2004; Daume III and Marcu, 2006), self-taught learning (Raina et al., 2007; Dai et al., 2008), 4 and unsupervised transfer learning (Wang et al., 2008).”
A Comprehensive Survey on Transfer Learning (2019, background): “[42] also proposed an unsupervised clustering approach, which is termed as self-taught clustering (STC).”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, neutral): “These tasks include clustering, dimensionality reduction and density estimation [55, 247].”
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification (2009, background): “Transfer learning can be categorized as (1) inductive transfer: there are a few labeled data in the target task, while there are a large amount of labeled [18] [19] [20] or unlabeled [21] data in the source task, (2) transductive transfer: there are no labeled data in the target task, while there ar”
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.”
Multitask Bregman clustering (2010, background): “In addition, there are recent work [17,18] on learning a shared feature representations across multiple learning tasks, which are also supervised.”
OTL: A Framework of Online Transfer Learning (2010, background): “…domains; trans-ductive TL (Arnold et al., 2007) aims to extract the knowledge from source domain to improve the prediction tasks in the target domain without labeled data in the target domain; while unsupervised TL aims to resolve unsupervised learning tasks in target domain (Dai et al., 2008).”
Convex Discriminative Multitask Clustering (2013, background): “Only very recently, it received more and more attention [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47].”
A Survey on Transfer Learning (2010, methodology): “STC tries to learn a common feature space across domains, which helps in clustering in the target domain.”
Smart Multitask Bregman Clustering and Multitask Kernel Clustering (2015, background): “In unsupervised transfer learning, there are no labeled data in both the target and source domains [Dai et al. 2008; Jiang and Chung 2012].”
Multi-Task Multi-View Clustering (2016, methodology): “For multi-task relationship learning, we use co-clustering to learn a shared subspace among the related tasks under each common view, which is based on the observation that related tasks often share some common or relevant features [60].”
Robust and Discriminative Self-Taught Learning (2013, methodology): “Self-taught learning is an emerging branch of transfer learning, which was first formalized in (Raina et al., 2007) and further developed in (Dai et al., 2008; Raina, 2009; Lee et al., 2009).”
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].”
Transfer learning: a friendly introduction (2022, background): “In this TL scenario, the target and the source task are different but somehow related, similar to the inductive TL. Unsupervised TL, on the other hand, focuses more on completing unsupervised tasks, such as clustering and dimension reduction [13, 14].”
Translated Learning: Transfer Learning across Different Feature Spaces (2008, background): “Transfer learning mainly focuses on training and testing processes being in different scenarios, e.g. multi-task learning [3], learning with auxiliary data sources [11], learning from irrelevant categories [10], and self-taught learning [9, 4].”
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.”
Feature-based transfer learning with real-world applications (2010, background): “[50] studied a new case of clustering problems, known as self-taught clustering.”
Heterogeneous Transfer Learning for Image Clustering via the SocialWeb (2009, methodology): “In addition, we compared our algorithm aPLSA to a state-of-the-art transfer clustering strategy, known as self-taught clustering ( STC ) (Dai et al., 2008b).”