Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties

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Accelerated Attributed Network Embedding (2017, methodology): “[11] introduced several efficient algorithms for convex clustering with three most commonly used norms, `1, `2 and `∞.”
The joint graphical lasso for inverse covariance estimation across multiple classes (2011, methodology): “A very eﬃcient algorithm for this special case, which can be performed in O ( K log K ) operations, is available (Hocking et al. 2011, Hoeﬂing 2010 a , Tibshirani 2012).”
Robust continuous clustering (2017, methodology): “Our formulation is based on recent convex relaxations for clustering (25, 26).”
Learning with Submodular Functions: A Convex Optimization Perspective (2011, methodology): “This may have applications to multivariate outlier detection by considering extensions similar to [99].”
COMIC: Multi-view Clustering Without Parameter Selection (2019, methodology): “Convex clustering (Hocking et al., 2011; Flammarion et al., 2017) projects the data point into another space by incorporating some convex pairwise fusion penalties.”
Network Lasso: Clustering and Optimization in Large Graphs (2015, methodology): “For λ ’s in between λ = 0 and λ critical , the family of solutions is known as the regularization path , though it is sometimes referred to as the clusterpath [14].”