IKFeature {isokernel}

Build Isolation Kernel feature vector representations via the feature map for a given dataset.

Description

Isolation kernel is a data dependent kernel measure that is adaptive to local data distribution and has more flexibility in capturing the characteristics of the local data distribution. It has been shown promising performance on density and distance-based classification and clustering problems.

This version uses Voronoi diagrams to split the data space and calculate Isolation kernel Similarity, following the paper: Qin, X., Ting, K.M., Zhu, Y. and Lee, V.C., 2019, July. Nearest-neighbour-induced isolation similarity and its impact on density-based clustering. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 4755-4762). Based on this implementation, the feature in the Isolation kernel space is the index of the cell in Voronoi diagrams. Each point is represented as a binary vector such that only the cell the point falling into is 1.

Usage

IKFeature(data, Sdata = data, psi = 64, t = 200, Sp = TRUE)

Arguments

data

A dataset used for applying Isolation kernel function. The data is a n by d matrix, where n is the data size, d is the dimensionality.

Sdata

The dataset use for generating Voronoi diagrams, it can be the same as the input data.

psi

The number of cells in each Voronoi diagram, it should be large if there are more clusters or more complex structures in the data. It could be [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024].

t

The number of Voronoi diagrams, the higher the more stable the result.

Sp

Indicating whether return the features as a sparse matrix.

Value

The finite binary features based on the kernel feature map. The features are organised as a n by psi*t matrix.

Examples

library(isokernel)
df <- matrix(1:50, nrow = 5, ncol = 10)
IKFeatures <- IKFeature(data=df,psi=4,t=200)