- Tytuł:
- Conditional mean embedding and optimal feature selection via positive definite kernels
- Autorzy:
-
Jorgensen, Palle E.T.
Song, Myung-Sin
Tiang, James - Powiązania:
- https://bibliotekanauki.pl/articles/29519641.pdf
- Data publikacji:
- 2024
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
positive-definite kernels
reproducing kernel Hilbert space
stochastic processes
frames
machine learning
embedding problem
optimization - Opis:
- Motivated by applications, we consider new operator-theoretic approaches to conditional mean embedding (CME). Our present results combine a spectral analysis-based optimization scheme with the use of kernels, stochastic processes, and constructive learning algorithms. For initially given non-linear data, we consider optimization-based feature selections. This entails the use of convex sets of kernels in a construction of optimal feature selection via regression algorithms from learning models. Thus, with initial inputs of training data (for a suitable learning algorithm), each choice of a kernel K in turn yields a variety of Hilbert spaces and realizations of features. A novel aspect of our work is the inclusion of a secondary optimization process over a specified convex set of positive definite kernels, resulting in the determination of “optimal” feature representations.
- Źródło:
-
Opuscula Mathematica; 2024, 44, 1; 79-103
1232-9274
2300-6919 - Pojawia się w:
- Opuscula Mathematica
- Dostawca treści:
- Biblioteka Nauki
Artykuł