Semi-Supervised Kernel Mean Shift Clustering

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Learning to cluster in order to Transfer across domains and tasks (2017, methodology): “We compare this with the baseline approach SKMS (Anand et al., 2014), which does not require a given K and supports a pipeline to estimate K automatically (therefore we only put it into the column K = 100 in table 1.”
Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation (2022, methodology): “Second, we deploy unsupervised clustering methods to identify some centers and mean shift (Anand et al., 2013) for the embeddings using model t − 1 .”
Multi-class Classification without Multi-class Labels (2019, unknown): “…(Cai et al., 2009) 22.2% 16.3% 0.372 0.498 LSC (Chen & Cai, 2011) 23.6% 18.0% 0.376 0.500 ITML (Davis et al., 2007) 56.7% 47.2% 0.674 0.727 SKKm (Anand et al., 2014) 62.4% 46.9% 0.770 0.781 SKLR (Amid et al., 2016) 66.9% 46.8% 0.791 0.760 CSP (Wang et al., 2014) 62.5% 65.4% 0.812 0.812…”
Semi-supervised Kernel Metric Learning Using Relative Comparisons (2016, methodology): “We select the SK k m variant as Anand et al. [2014] have shown that SK k m tends to produce more accurate results than other semi-supervised clustering methods.”