M3IC: Maximum Margin Multiple Instance Clustering

By Dan Zhang, Fei Wang, Luo Si, Tao Li. 2009

39 citations recorded by the source corpus.

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Citations in the corpus

Learning Theory for Distribution Regression (2014, neutral): “…including (i) regression (Ray and Page, 2001; Dooly et al., 2002; Zhou et al., 2009; Kwok and Cheung, 2007), (ii) clustering (Zhang and Zhou, 2009; Zhang et al., 2009, 2011; Chen and Wu, 2012), (iii) ranking (Bergeron et al., 2008; Hu et al., 2008; Bergeron et al., 2012), (iv) outlier…”
Mining And-Or Graphs for Graph Matching and Object Discovery (2015, methodology): “Object discovery & co-segmentation: Operation 1 in each model-mining iteration can be regarded as a process of object discovery; therefore, we compared the object discovery performance between the mined AoG and six recent methods for object discovery and co-segmentation: saliency-guided multiple cla”
Weakly supervised histopathology cancer image segmentation and classification (2014, background): “The current literature in MIL assumes single cluster/model/ classifier for the target of interest (Viola et al., 2005), single cluster within each bag (Babenko et al., 2008; Zhang and Zhou, 2009; Zhang et al., 2009), or multiple components of one object (Dollár et al., 2008).”
Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering (2011, background): “This paper substantially extends our preliminary work in [35].”
Model-Based Multiple Instance Learning (2017, methodology): “igated. To the best of our knowledge, there are two clustering algorithms for point patterns: the Bag-level MI Clustering (BAMIC) algorithm [54]; and the Maximum Margin MI Clustering (M3IC) algorithm [55]. BAMIC adapts the k-medoids algorithm with the Hausdorff distance as a measure of dissimilarity”
Clustering for point pattern data (2017, methodology): “knowledge, there are two clustering algorithms for point patterns: the Bag-level Multi-instance Clustering (BAMIC) algorithm [27]; and the Maximum Margin Multiple Instance Clustering (M3IC) algorithm [28]. BAMIC adapts the k-medoids algorithm for the clustering of point pattern data (or multiple ins”