Robust Low Rank Kernel Embeddings of Multivariate Distributions

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Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “By assuming that the latent structure underlying the datagenerating process has a low-rank structure, e.g., latent tree, Song and Dai (2013) constructs an improved estimator of the kernel mean embedding for multivariate distribution using the truncated SVD (TSVD) algorithm.”
Kernel Mean Estimation via Spectral Filtering (2014, methodology): “This approach has been used in [21] to improve the kernel mean estimation under the low-rank as sumption.”
Deep Transfer Learning with Joint Adaptation Networks (2016, background): “,X) is unknown, we can estimate its embedding using a finite sample (Song et al., 2013).”
Interpretable bilinear attention network with domain adaptation improves drug–target prediction (2022, unknown): “The multilinear map captures multiplicative interactions between two independent distributions 49,50 .”
Conditional Adversarial Domain Adaptation (2017, methodology): “And the multilinear map of infinite-dimensional nonlinear feature maps has been successfully applied to embed joint distribution or conditional distribution into reproducing kernel Hilbert spaces [47, 44, 45, 30].”
Nonparametric Estimation of Multi-View Latent Variable Models (2013, result): “However, none of the previous works explicitly recovers the actual parameters of the models Song et al. (2011); Song & Dai (2013); Sgouritsa et al. (2013).”