Multi-instance clustering with applications to multi-instance prediction

By Min-Ling Zhang, Zhi-Hua Zhou. 2009

153 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning Theory for Distribution Regression (2014, methodology): “…problems in the literature 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.,…”
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).”
Model-Based Multiple Instance Learning (2017, methodology): “BAMIC adapts the k-medoids algorithm with the Hausdorff distance as a measure of dissimilarity between point patterns [54].”
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning (2013, methodology): “This extension of ReliefF for MIL uses the average Hausdorff distance (57) proposed by Zhang and Zhou to measure the distance between two bags.”
Revisiting Multiple-Instance Learning Via Embedded Instance Selection (2008, methodology): “Since then, many other MI learning algorithms have been proposed (see, for example, [1], [2], [10], [12], [14], [24], [25], [27], [28], [29], [30]).”
Multi-instance multi-label learning (2008, background): “Recently, research on multi-instance clustering [82], multi-instance semi-supervised learning [49] and multi-instance active learning [60] have also been reported.”
Multiple instance classification: Review, taxonomy and comparative study (2013, methodology): “Other multi-instance problems include multi-instance reg ression [38, 35, 39], multi-instance multi-label learning [40] and multi-instance clustering [ 35, 41].”
Multiple instance learning: A survey of problem characteristics and applications (2016, methodology): “k -Medoids and the Hausdorff distance [48]).”
A review of multi-instance learning assumptions (2010, methodology): “The MIGraph and miGraph algorithms (Zhou et al ., 2009) apply support vector machines to MI data by using graph kernels on the e -graphs of the bags.”
Clustering for point pattern data (2017, methodology): “BAMIC adapts the k-medoids algorithm for the clustering of point pattern data (or multiple instance data) by using the Hausdorff metric as a measure of dissimilarity between two point patterns [27].”
Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering (2011, methodology): “We have conducted comprehensive performance evaluations by testing our method and comparing it with the only existed MIC method–BAMIC [33] and two other single instance learning methods, i.”
Identifying Multi-instance Outliers (2010, unknown): “Many traditional learning tasks have been brought into multi-instance framework and yields several new machine learning issues such as multi-instance ranking [14, 17], multi-instance clustering [18, 19], etc.”
Contextual Hausdorff dissimilarity for multi-instance clustering (2012, unknown): “The two sets are realworld data, which are generated in the research of drug activity prediction [2].”