Manifold Alignment without Correspondence

By Chang Wang, Sridhar Mahadevan. 2009

210 citations recorded by the source corpus.

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Citations in the corpus

Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning (2017, neutral): “We also compare to several linear methods, including random projections, canonical correlation analysis (CCA), and unsupervised manifold alignment (UMA).”
Optimal Transport for Domain Adaptation (2014, methodology): “lations [32] or by using pairwise constraints [50].”
Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment (2015, methodology): “Learning an Inter-State Mapping Unsupervised Manifold Alignment (UMA) is a technique that efficiently discovers an alignment between two datasets (Wang & Mahadevan 2009).”
Domain Adaptation with Regularized Optimal Transport (2014, methodology): “The representation transfer can be performed by matching the means of the domains in the feature space [10], aligning the domains by their correlations [11] or by using pairwise constraints [12].”
Domain adaptation for object recognition: An unsupervised approach (2011, methodology): “Another related method by Wang and Mahadevan [39] pose this problem in terms of unsupervised manifold alignment, where the manifolds on which the source and target domain lie are aligned by preserving a notion of the 'neighborhood structure' of the data points.”
Transfer Learning with Graph Co-Regularization (2012, methodology): “Recently, several methods have been proposed to explore the geometric structure for transfer learning [13], [14], [15], [16], [17].”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, background): “On the downside, SS–MA requires few labeled pixels in all the domains aligned (studies on how to relax this assumption can be found in [159] and [160]).”
Semi-supervised Domain Adaptation with Subspace Learning for visual recognition (2015, background): “In another work by Wang et al. [26], the problem was considered in terms of unsupervised manifold alignment, where the source and target domains were aligned by preserving the neighborhood structure of the data points.”
Transfer Learning in Deep Reinforcement Learning: A Survey (2020, methodology): “Another work is [115] which applied the Unsupervised Manifold Alignment (UMA) method [117] to automatically learn the state mapping.”
Unsupervised Visual Domain Adaptation Using Subspace Alignment (2013, methodology): “Our method is also related to manifold alignment [16, 17, 18] whose main objective is to align two datasets from two different manifolds such that they can be projected to a common subspace.”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, background): “data is not available, the study of [217] learns the mappings to”
Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation (2013, background): “Manifold-alignment based DA [31] computes similarity between data points in different domains through the local geometry of data points within each domain.”