get_kernel_mean_embedding

The function to calculate Maxima weighted kernel mean mapping for Isolation Kernel in RKHS related to parameters space

internal

Incorporates Approximate Bayesian Computation to get a posterior distribution and to select a model optimal parameter for an observation point. Additionally, the meta-sampling heuristic algorithm is realized for parameter estimation, which requires no model runs and is dimension-independent. A sampling scheme is also presented that allows model runs and uses the meta-sampling for point generation. A predictor is realized as the meta-sampling for the model output. All the algorithms leverage a machine learning method utilizing the maxima weighted Isolation Kernel approach, or 'MaxWiK'. The method involves transforming raw data to a Hilbert space (mapping) and measuring the similarity between simulated points and the maxima weighted Isolation Kernel mapping corresponding to the observation point. Comprehensive details of the methodology can be found in the papers Iurii Nagornov (2024) <doi:10.1007/978-3-031-66431-1_16> and Iurii Nagornov (2023) <doi:10.1007/978-3-031-29168-5_18>.

Yuri Nagornov

MaxWiK

Machine Learning Method Based on Isolation Kernel Mean Embedding

get_kernel_mean_embedding function

<dl><dt>parameters_Matrix_iKernel</dt>
<dd>Matrix of all the points represented in RKHS related to parameters space</dd>
<dt>Hilbert_weights</dt>
<dd>Maximal weights in RKHS to get related part of kernel mean embedding from parameters_Matrix_iKernel</dd></dl>

Arguments

The function to calculate Maxima weighted kernel mean mapping for Isolation Kernel in RKHS related to parameters space — get_kernel_mean_embedding

<dl>

<dt>parameters_Matrix_iKernel</dt>
<dd>Matrix of all the points represented in RKHS related to parameters space</dd>


<dt>Hilbert_weights</dt>
<dd>Maximal weights in RKHS to get related part of kernel mean embedding from parameters_Matrix_iKernel</dd>

</dl>

get_kernel_mean_embedding: The function to calculate Maxima weighted kernel mean mapping for Isolation Kernel in RKHS related to parameters space

Description

Usage

Value

Arguments

Examples