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Kernel embedding of distributions
In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in…
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Related topics
Related topics
32 relations
Basis function
Belief propagation
Bernhard Schölkopf
Bregman divergence
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Broader (1)
Machine learning
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding
Haoliang Li
,
Sinno Jialin Pan
,
Renjie Wan
,
A. Kot
AAAI Conference on Artificial Intelligence
2019
Corpus ID: 69987286
Heterogeneous Transfer Learning (HTL) aims to solve transfer learning problems where a source domain and a target domain are of…
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2018
2018
Unsupervised Object Matching for Relational Data
Tomoharu Iwata
,
N. Ueda
ArXiv
2018
Corpus ID: 52945328
We propose an unsupervised object matching method for relational data, which finds matchings between objects in different…
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2017
2017
Kernel embedding-based state estimation for colored noise systems
Kyuman Lee
,
Youngjun Choi
,
Eric N. Johnson
Symposium on Dependable Autonomic and Secure…
2017
Corpus ID: 23771339
A required assumption of a Kalman filter, the most-widely-used state estimator in avionic systems, is the Gaussian and whiteness…
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Highly Cited
2016
Highly Cited
2016
Mixture Proportion Estimation via Kernel Embeddings of Distributions
H. G. Ramaswamy
,
C. Scott
,
Ambuj Tewari
International Conference on Machine Learning
2016
Corpus ID: 7895536
Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given…
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2016
2016
Probability Inequalities for Kernel Embeddings in Sampling without Replacement
Markus Schneider
International Conference on Artificial…
2016
Corpus ID: 17605807
The kernel embedding of distributions is a popular machine learning technique to manipulate probability distributions and is an…
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2015
2015
Expected similarity estimation for large scale anomaly detection
Markus Schneider
,
W. Ertel
,
G. Palm
IEEE International Joint Conference on Neural…
2015
Corpus ID: 18154235
We propose a new algorithm named EXPected Similarity Estimation (EXPoSE) to approach the problem of anomaly detection (also known…
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2014
2014
Monte Carlo Filtering Using Kernel Embedding of Distributions
Motonobu Kanagawa
,
Yu Nishiyama
,
A. Gretton
,
K. Fukumizu
AAAI Conference on Artificial Intelligence
2014
Corpus ID: 11312408
Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert…
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2014
2014
Scalable Kernel Embedding of Latent Variable Models
Le Song
2014
Corpus ID: 16357379
Kernel embedding of distributions maps distributions to the reproducing kernel Hilbert space (RKHS) of a kernel function, such…
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2013
2013
Robust Low Rank Kernel Embeddings of Multivariate Distributions
Le Song
,
Bo Dai
NIPS
2013
Corpus ID: 7123090
Kernel embedding of distributions has led to many recent advances in machine learning. However, latent and low rank structures…
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