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Table 2 Surveyed HTL methods which require limited target labels and accept unlabeled target data

From: A survey on heterogeneous transfer learning

Methods Characteristics Sections
DAMA [27] Symmetric, manifold alignment with labels, multi-source DAMA
CDLS [46] Symmetric and landmark weights CDLS
IDL for HDA [49] Online tasks, symmetric eigenanalysis-based IDL for HDA
MOMAP [51] Asymmetric, mapping by rotation and translation, multi-class, multi-source MOMAP
HeMap [26] Symmetric, spectral mapping Bayesian method, cluster-based sampling HeMap
Proactive HTL [54] Symmetric, label embeddings, proactive learning Proactive HTL
SHFA [47] Symmetric transformation w/augmentation for semi-supervised SHFA
CT-Learn [57] Requires co-occurrence data, joint transition probability graph, Markov random walk, multi-source CT-Learn
SSKMDA [60] Instance-based asymmetric, kernel matching SSKMDA
SCP-ECOC [62] Symmetric, multi-class, ECOC scheme SCP-ECOC
MMDT [48] Asymmetric, image, max-margin, multi-class MMDT
SSMVCCAE [64] Symmetric, multi-view ensemble, CCA analysis, SRKDA SMVCCAE, SSMVCCAE
TNT [65] Neural network-based mapping and classification TNT
HDANA [67] Symmetric, deep learning, autoencoder mapping HDANA