# basis_odp: The basis of Orthogonal Discriminant Projection (ODP) In spinifex: Manual Tours, Manual Control of Dynamic Projections of Numeric Multivariate Data

## Description

Orthogonal Discriminant Projection (ODP) is a linear dimension reduction method with class supervision. It maximizes weighted difference between local and non-local scatter while local information is also preserved by constructing a neighborhood graph.

## Usage

 `1` ```basis_odp(data, class, d = 2, type = c("proportion", 0.1), ...) ```

## Arguments

 `data` Numeric matrix or data.frame of the observations, coerced to matrix. `class` The class for each observation, coerced to a factor. `d` Number of dimensions in the projection space. of `class`. `type` A vector specifying the neighborhood graph construction. Expects; `c("knn", k)`, `c("enn", radius)`, or `c("proportion",ratio)`. Defaults to `c("knn", sqrt(nrow(data)))`, nearest neighbors equal to the square root of observations. `...` Optional, other arguments to pass to `Rdimtools::do.odp`.

## References

Li B, Wang C, Huang D (2009). "Supervised feature extraction based on orthogonal discriminant projection." Neurocomputing, 73(1-3), 191-196.

`Rdimtools::do.odp` for locality preservation arguments.

`Rdimtools::aux.graphnbd` for details on `type`.

Other basis identifiers: `basis_guided()`, `basis_half_circle()`, `basis_olda()`, `basis_onpp()`, `basis_pca()`

## Examples

 ```1 2 3``` ```dat_std <- scale_sd(wine[, 2:6]) clas <- wine\$Type basis_odp(data = dat_std, class = clas) ```

spinifex documentation built on Sept. 28, 2021, 5:09 p.m.