Skip to main content

Table 1 Main features of multi-task metric learning methods

From: A review on multi-task metric learning

Name Year Multi-task Strategy Solver Dimension Reduction Side-information Regularizer
mt-LMNN 2010 Shared composition Projected gradient descent No Triplets Frobenius norm
TML 2010 Task relationship learning Alternating Optimization No Pairs Task covariance
mtMLCS 2011 Shared subspace Gradient descent Yes Triplets -
M2SL 2012 Shared composition Coordinate gradient descent No Pairs Frobenius norm
GPmtML 2012 Geometry preserving Alternating optimization No Triplets Von Neumann divergence
mt-SCML 2014 Shared sparse representation Regularized dual averaging Yes Triplets 2/1 norm
MtMCML 2014 Graph regularization Alternating optimization No Pairs Laplacian
TMTL 2015 Metric weighted sum Direct calculation No Covariance -
online-SMDM 2016 Shared composition Online projected gradient descent No Pairs Frobenius norm
CP-mtML 2016 Coupled projection Stochastic gradient projection Yes Pairs -
DMML 2016 Shared subnetwork Sub-gradient descent No Pairs -
HMML 2017 Shared composition Not mentioned No Triplets Trace norm
mtDCML 2017 Shared network Gradient descent No Pairs -