Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Wells, Lennie"'
Autor:
Flamich, Gergely, Wells, Lennie
Channel simulation algorithms can efficiently encode random samples from a prescribed target distribution $Q$ and find applications in machine learning-based lossy data compression. However, algorithms that encode exact samples usually have random ru
Externí odkaz:
http://arxiv.org/abs/2405.04363
Recent developments in regularized Canonical Correlation Analysis (CCA) promise powerful methods for high-dimensional, multiview data analysis. However, justifying the structural assumptions behind many popular approaches remains a challenge, and fea
Externí odkaz:
http://arxiv.org/abs/2403.02979
The Canonical Correlation Analysis (CCA) family of methods is foundational in multiview learning. Regularised linear CCA methods can be seen to generalise Partial Least Squares (PLS) and be unified with a Generalized Eigenvalue Problem (GEP) framewor
Externí odkaz:
http://arxiv.org/abs/2310.01012
Generalized Eigenvalue Problems (GEPs) encompass a range of interesting dimensionality reduction methods. Development of efficient stochastic approaches to these problems would allow them to scale to larger datasets. Canonical Correlation Analysis (C
Externí odkaz:
http://arxiv.org/abs/2211.11323
Autor:
Wells, Lennie
Publikováno v:
The Mathematical Gazette; November 2021, Vol. 105 Issue: 564 p573-574, 2p
