kme: Calculate the kernel mean embedding matrix

In mildsvm: Multiple-Instance Learning with Support Vector Machines

Calculate the kernel mean embedding matrix

Description

Function to calculate the kernel mean embedding for to distributional data sets. It uses the empirical approximation for the integral

\int_{\mathcal X} \int_{\mathcal Y} K(x, y) d P_X d Q_Y

for a given kernel K(\cdot, \cdot). Currently only supports radial basis function kernel for fast computation.

Usage

## Default S3 method:
kme(df, df2 = NULL, sigma = 0.05, ...)

## S3 method for class 'mild_df'
kme(df, df2 = NULL, sigma = 0.05, ...)

Arguments

df	A data.frame of mild_df object, must have column 'instance_name' which defines the instances.
df2	A data.frame, mild_df object, or NULL (default NULL).
sigma	The parameter for 'radial' kernel (default 0.05).
...	Additional arguments passed to methods.

Details

If df2 = NULL, calculate the kernel mean embedding matrix of (df, df) otherwise calculate (df, df2)

Value

A matrix of kernel mean embedding at the instance level.

Methods (by class)

• default: Default S3 method

• mild_df: S3 method for class mild_df

Author(s)

Yifei Liu, Sean Kent

Examples

x = data.frame('instance_name' = c('inst_1', 'inst_2', 'inst_1'),
               'X1' = c(-0.4, 0.5, 2))
kme(x)

mild_df1 <- generate_mild_df(nbag = 10, positive_degree = 3)
kme(mild_df1)

mildsvm documentation built on July 14, 2022, 9:08 a.m.