| Title: | Distance Metrics for High-Dimensional Clustering |
|---|---|
| Description: | We provide three distance metrics for measuring the separation between two clusters in high-dimensional spaces. The first metric is the centroid distance, which calculates the Euclidean distance between the centers of the two groups. The second is a ridge Mahalanobis distance, which incorporates a ridge correction constant, alpha, to ensure that the covariance matrix is invertible. The third metric is the maximal data piling distance, which computes the orthogonal distance between the affine spaces spanned by each class. These three distances are asymptotically interconnected and are applicable in tasks such as discrimination, clustering, and outlier detection in high-dimensional settings. |
| Authors: | Jung Ae Lee [aut, cre], Jeongyoun Ahn [aut] |
| Maintainer: | Jung Ae Lee <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.2 |
| Built: | 2025-01-31 18:27:38 UTC |
| Source: | https://github.com/cran/distanceHD |
We provide three distance metrics for measuring the separation between two clusters in high-dimensional spaces. The first metric is the centroid distance, which calculates the Euclidean distance between the centers of the two groups. The second is a ridge Mahalanobis distance, which incorporates a ridge correction constant, alpha, to ensure that the covariance matrix is invertible. The third metric is the maximal data piling distance, which computes the orthogonal distance between the affine spaces spanned by each class. These three distances are asymptotically interconnected and are applicable in tasks such as discrimination, clustering, and outlier detection in high-dimensional settings.
Jung Ae Lee <[email protected]>; Jeongyoun Ahn <[email protected]>
Maintainer: Jung Ae Lee <[email protected]>
1. Ahn J, Marron JS (2010). The maximal data piling direction for discrimination. Biometrika, 97(1):254-259.
2. Ahn J, Lee MH, Yoon YJ (2012). Clustering high dimension, low sample size data using the maximal data piling distance. Statistica Sinica, 22(2):443-464.
3. Ahn J, Lee MH, Lee JA (2019). Distance-based outlier detection for high dimension, low sample size data. Journal of Applied Statistics.46(1):13-29.
Calculate the Centroid (Euclidean) distance between two groups in a high-dimensional space (d > n), with support for a single-member cluster. It also works in low-dimensional settings.
dist_cen(x, group)dist_cen(x, group)
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group |
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A numeric value of distance
Jung Ae Lee <[email protected]>
data(leukemia) group = leukemia$Y # 38 patients status with a value of 1 or 2 x = leukemia$X # 38 by 3051 genes # apply the function dist_cen(x, group) # 25.4data(leukemia) group = leukemia$Y # 38 patients status with a value of 1 or 2 x = leukemia$X # 38 by 3051 genes # apply the function dist_cen(x, group) # 25.4
Calculate the Mahalanobis distance between two groups in a high-dimensional space (d > n), using a ridge correction on the covariance matrix to ensure invertibility. The method also supports a single-member cluster and works in low-dimensional settings without a ridge correction.
dist_mah(x, group, alpha)dist_mah(x, group, alpha)
x |
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group |
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alpha |
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A numeric value of distance
Jung Ae Lee <[email protected]>
data(leukemia) group = leukemia$Y # 38 patients status with a value of 1 or 2 x = leukemia$X # 38 x 3051 genes # apply the function dist_mah(x, group) # 26.8 # default alpha d = 3051; n = 38 alpha = sqrt(log(d)/n) dist_mah(x, group, alpha) # 26.8data(leukemia) group = leukemia$Y # 38 patients status with a value of 1 or 2 x = leukemia$X # 38 x 3051 genes # apply the function dist_mah(x, group) # 26.8 # default alpha d = 3051; n = 38 alpha = sqrt(log(d)/n) dist_mah(x, group, alpha) # 26.8
Calculate the MDP (maximal data piling) distance between two groups in a high-dimensional space (d > n), with support for a single-member cluster. It also works in low-dimensional settings.
dist_mdp(x, group)dist_mdp(x, group)
x |
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group |
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A numeric value of distance
Jeongyoun Ahn <[email protected]>
data(leukemia) group = leukemia$Y # 38 patients status with a value of 1 or 2 x = leukemia$X # 38 x 3051 genes # apply the function dist_mah(x, group) # 26.8data(leukemia) group = leukemia$Y # 38 patients status with a value of 1 or 2 x = leukemia$X # 38 x 3051 genes # apply the function dist_mah(x, group) # 26.8
Gene expression data (3051 genes and 38 tumor mRNA samples) from the leukemia microarray study of Golub et al. (1999). )
data(leukemia)data(leukemia)
A list with the following elements:
X: a (38 by 3051) matrix giving the expression levels of 3051 genes for 38 leukemia patients. Each row corresponds to a patient, each column to a gene.
Y: a numeric vector of length 38 giving the cancer class of each patient.
gene.names: a matrix containing the names of the 3051 genes for the gene expression matrix X. The three columns correspond to the gene index, ID, and Name, respectively.
The data are described in Golub et al. (1999) and can be freely downloaded from http://www.broadinstitute.org/cgi-bin/cancer/publications/pub_paper.cgi?paper_id=43.
S. Dudoit, J. Fridlyand and T. P. Speed (2002). Comparison of discrimination methods for the classification of tumors using gene expression data, Journal of the American Statistical Association 97, 77-87.
Golub et al. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science 286, 531-537.
plsgenomics::leukemia
data(leukemia) # how many samples and genes? dim(leukemia$X) # how many samples of class 1 and 2, respectively? table(leukemia$Y)data(leukemia) # how many samples and genes? dim(leukemia$X) # how many samples of class 1 and 2, respectively? table(leukemia$Y)