Exploiting Known Taxonomies in Learning Overlapping Concepts

By Deng Cai, Xiaofei He, Kun Zhou, Jiawei Han, Hujun Bao. International Joint Conference on Artificial Intelligence, 2007

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Efficient max-margin multi-label classification with applications to zero-shot learning (2012, methodology): “Methods such as Cai and Hofmann (2007), Cesa-Bianchi et al. (2006) optimise a hierarchical loss over the tree structure but do not incorporate pairwise, or higher order, label interaction terms.”
Fixed-dimensional acoustic embeddings of variable-length segments in low-resource settings (2013, methodology): “This is analogous to LSDA, but where we linearly combine the normalized Laplacians of within- and betweenclass graphs rather than the adjacency matrices.”
Supervised patient similarity measure of heterogeneous patient records (2012, methodology): “For LSML,LSDAand Comdi, we fx |NComdi, we use Algorithm 1 with m=30, and . is set by cross val- For oi |= |N ei |=5.”
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching (2011, methodology): “To alleviate the above problem, a graph-embedding framework can be used [7, 24, 30].”
Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis (2014, methodology): “Typical methods include MFA [36], locality sensitive discriminant analysis [39], local Fisher discriminant analysis [40], [41] and robust linearly optimized discriminant analysis [42].”
Label Refinery: Improving ImageNet Classification through Label Progression (2018, background): “Incorporating taxonomic information directly into the model’s architecture is explored in [18].”