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https://github.com/terrytangyuan/dml
R package for Distance Metric Learning
https://github.com/terrytangyuan/dml
dimensionality-reduction distance-metric-learning machine-learning metric-learning r statistics
Last synced: 23 days ago
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R package for Distance Metric Learning
- Host: GitHub
- URL: https://github.com/terrytangyuan/dml
- Owner: terrytangyuan
- License: mit
- Created: 2015-08-26T12:35:54.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2023-07-07T16:55:45.000Z (over 1 year ago)
- Last Synced: 2024-12-16T21:57:50.204Z (25 days ago)
- Topics: dimensionality-reduction, distance-metric-learning, machine-learning, metric-learning, r, statistics
- Language: R
- Size: 254 KB
- Stars: 58
- Watchers: 17
- Forks: 31
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
**Note**: This package has been maintained by [@terrytangyuan](https://github.com/terrytangyuan) since 2015. Please [consider sponsoring](https://github.com/sponsors/terrytangyuan)!
[](https://doi.org/10.21105/joss.01036)
[](https://cran.r-project.org/package=dml)
[](https://coveralls.io/r/terrytangyuan/dml?branch=master)
[](https://cran.r-project.org/package=dml)
[](https://badges.mit-license.org)
[](https://zenodo.org/badge/latestdoi/41424588)
# dml (Distance Metric Learning in R)
R package for a collection of *Distance Metric Learning* algorithms, including global and local methods such as *Relevant Component Analysis*, *Discriminative Component Analysis*, *Local Fisher Discriminant Analysis*, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
## Installation
Install the current release from CRAN:
```r
install.packages("dml")
```
Or, try the latest development version from GitHub:
```r
devtools::install_github("terrytangyuan/dml")
```
## Examples
### Relevant Component Analysis
```r
library("MASS")
# generate synthetic multivariate normal data
set.seed(42)
k <- 100L # sample size of each class
n <- 3L # specify how many classes
N <- k * n # total sample size
x1 <- mvrnorm(k, mu = c(-16, 8), matrix(c(15, 1, 2, 10), ncol = 2))
x2 <- mvrnorm(k, mu = c(0, 0), matrix(c(15, 1, 2, 10), ncol = 2))
x3 <- mvrnorm(k, mu = c(16, -8), matrix(c(15, 1, 2, 10), ncol = 2))
x <- as.data.frame(rbind(x1, x2, x3)) # predictors
y <- gl(n, k) # response
# fully labeled data set with 3 classes
# need to use a line in 2D to classify
plot(x[, 1L], x[, 2L],
bg = c("#E41A1C", "#377EB8", "#4DAF4A")[y],
pch = rep(c(22, 21, 25), each = k)
)
abline(a = -10, b = 1, lty = 2)
abline(a = 12, b = 1, lty = 2)
```
```r
# generate synthetic chunklets
chunks <- vector("list", 300)
for (i in 1:100) chunks[[i]] <- sample(1L:100L, 10L)
for (i in 101:200) chunks[[i]] <- sample(101L:200L, 10L)
for (i in 201:300) chunks[[i]] <- sample(201L:300L, 10L)
chks <- x[unlist(chunks), ]
# make "chunklet" vector to feed the chunks argument
chunksvec <- rep(-1L, nrow(x))
for (i in 1L:length(chunks)) {
for (j in 1L:length(chunks[[i]])) {
chunksvec[chunks[[i]][j]] <- i
}
}
# relevant component analysis
rcs <- rca(x, chunksvec)
# learned transformation of the data
rcs$A
#> [,1] [,2]
#> [1,] -3.181484 -0.8812647
#> [2,] -1.196200 2.3438640
# learned Mahalanobis distance metric
rcs$B
#> [,1] [,2]
#> [1,] 10.898467 1.740125
#> [2,] 1.740125 6.924592
# whitening transformation applied to the chunklets
chkTransformed <- as.matrix(chks) %*% rcs$A
# original data after applying RCA transformation
# easier to classify - using only horizontal lines
xnew <- rcs$newX
plot(xnew[, 1L], xnew[, 2L],
bg = c("#E41A1C", "#377EB8", "#4DAF4A")[gl(n, k)],
pch = c(rep(22, k), rep(21, k), rep(25, k))
)
abline(a = -15, b = 0, lty = 2)
abline(a = 16, b = 0, lty = 2)
```
### Other Examples
For examples of Local Fisher Discriminant Analysis, please take a look at the separate package [here](https://github.com/terrytangyuan/lfda). For examples of all other implemented algorithms, please take a look at the dml [package reference manual](https://cran.r-project.org/web/packages/dml/dml.pdf).
## Brief Introduction
Distance metric is widely used in the machine learning literature. We used to choose a distance metric according to a priori (Euclidean Distance , L1 Distance, etc.) or according to the result of cross validation within small class of functions (e.g. choosing order of polynomial for a kernel). Actually, with priori knowledge of the data, we could learn a more suitable distance metric with (semi-)supervised distance metric learning techniques. dml is such an R package aims to implement a collection of algorithms for (semi-)supervised distance metric learning. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
## Algorithms
Algorithms planned in the first development stage:
* Supervised Global Distance Metric Learning:
* Relevant Component Analysis (RCA) - implemented
* Kernel Relevant Component Analysis (KRCA)
* Discriminative Component Analysis (DCA) - implemented
* Kernel Discriminative Component Analysis (KDCA)
* Global Distance Metric Learning by Convex Programming - implemented
* Supervised Local Distance Metric Learning:
* Local Fisher Discriminant Analysis - implemented
* Kernel Local Fisher Discriminant Analysis - implemented
* Information-Theoretic Metric Learning (ITML)
* Large Margin Nearest Neighbor Classifier (LMNN)
* Neighbourhood Components Analysis (NCA)
* Localized Distance Metric Learning (LDM)
The algorithms and routines might be adjusted during developing.
## Contribute & Code of Conduct
To contribute to this project, please take a look at the [Contributing Guidelines](CONTRIBUTING.md) first. Please note that this project is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.
## Contact
Contact the maintainer of this package:
Yuan Tang