Results 11 to 20 of about 18,794 (253)
Subdomain Adaptation With Manifolds Discrepancy Alignment [PDF]
IEEE Transactions on Cybernetics, 2022 Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this paper, we take the local divergence of subdomains into account in transfer.
Pengfei Wei 0001 +3 more
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Manifold Alignment with Label Information
2023 International Conference on Sampling Theory and Applications (SampTA), 2023 Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integration of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called
Andrés F. Duque +3 more
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CoRR, 2020 Domain adaptation is an essential task in transfer learning to leverage data in one domain to bolster learning in another domain. In this paper, we present a new semi-supervised manifold alignment technique based on a two-step approach of projecting and filtering the source and target domains to low dimensional spaces followed by joining the two spaces.
Stefan Dernbach, Don Towsley
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Manifold-aligned Neighbor Embedding
CoRR, 2022 Accepted at the ICLR 2022 Workshop on Geometrical and Topological Representation ...
Mohammad Tariqul Islam 0003 +1 more
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Manifold Alignment Aware Ants: A Markovian Process for Manifold Extraction
Neural Computation, 2022 Abstract The presence of manifolds is a common assumption in many applications, including astronomy and computer vision. For instance, in astronomy, low-dimensional stellar structures, such as streams, shells, and globular clusters, can be found in the neighborhood of big galaxies such as the Milky Way.
Mohammad Mohammadi 0004 +2 more
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Fuzzy Granule Manifold Alignment Preserving Local Topology
IEEE Access, 2020 Granular computing has the advantage of discovering complex data knowledge, and manifold alignment has proven of great value in a lot of areas of machine learning.
Wei Li +6 more
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CoRR, 2015 We introduce a kernel method for manifold alignment (KEMA) and domain adaptation that can match an arbitrary number of data sources without needing corresponding pairs, just few labeled examples in all domains. KEMA has interesting properties: 1) it generalizes other manifold alignment methods, 2) it can align manifolds of very different complexities ...
Devis Tuia, Gustau Camps-Valls
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Adaptive Density Graph-Based Manifold Alignment for Fingerprinting Indoor Localization
IEEE Access, 2020 The received signal strength (RSS) fingerprint-based indoor localization has been considered as a promising solution, due to its relatively high localization accuracy and its ease of use in widespread Wireless Local Area Network (WLAN) infrastructure.
Shibao Li +5 more
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Unsupervised image translation with distributional semantics awareness
Computational Visual Media, 2023 Unsupervised image translation (UIT) studies the mapping between two image domains. Since such mappings are under-constrained, existing research has pursued various desirable properties such as distributional matching or two-way consistency.
Zhexi Peng +4 more
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Cell Reports: Methods, 2023 Summary: Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment,
Chenfeng He +12 more
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