# 6.6. Random Projection¶

The `sklearn.random_projection`

module implements a simple and
computationally efficient way to reduce the dimensionality of the data by
trading a controlled amount of accuracy (as additional variance) for faster
processing times and smaller model sizes. This module implements two types of
unstructured random matrix:
Gaussian random matrix and
sparse random matrix.

The dimensions and distribution of random projections matrices are controlled so as to preserve the pairwise distances between any two samples of the dataset. Thus random projection is a suitable approximation technique for distance based method.

## 6.6.1. The Johnson-Lindenstrauss lemma¶

The main theoretical result behind the efficiency of random projection is the Johnson-Lindenstrauss lemma (quoting Wikipedia):

In mathematics, the Johnson-Lindenstrauss lemma is a result concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space. The lemma states that a small set of points in a high-dimensional space can be embedded into a space of much lower dimension in such a way that distances between the points are nearly preserved. The map used for the embedding is at least Lipschitz, and can even be taken to be an orthogonal projection.

Knowing only the number of samples, the
`johnson_lindenstrauss_min_dim`

estimates
conservatively the minimal size of the random subspace to guarantee a
bounded distortion introduced by the random projection:

```
>>> from sklearn.random_projection import johnson_lindenstrauss_min_dim
>>> johnson_lindenstrauss_min_dim(n_samples=1e6, eps=0.5)
663
>>> johnson_lindenstrauss_min_dim(n_samples=1e6, eps=[0.5, 0.1, 0.01])
array([ 663, 11841, 1112658])
>>> johnson_lindenstrauss_min_dim(n_samples=[1e4, 1e5, 1e6], eps=0.1)
array([ 7894, 9868, 11841])
```

## 6.6.2. Gaussian random projection¶

The `GaussianRandomProjection`

reduces the
dimensionality by projecting the original input space on a randomly generated
matrix where components are drawn from the following distribution
\(N(0, \frac{1}{n_{components}})\).

Here a small excerpt which illustrates how to use the Gaussian random projection transformer:

```
>>> import numpy as np
>>> from sklearn import random_projection
>>> X = np.random.rand(100, 10000)
>>> transformer = random_projection.GaussianRandomProjection()
>>> X_new = transformer.fit_transform(X)
>>> X_new.shape
(100, 3947)
```

## 6.6.3. Sparse random projection¶

The `SparseRandomProjection`

reduces the
dimensionality by projecting the original input space using a sparse
random matrix.

Sparse random matrices are an alternative to dense Gaussian random projection matrix that guarantees similar embedding quality while being much more memory efficient and allowing faster computation of the projected data.

If we define `s = 1 / density`

, the elements of the random matrix
are drawn from

where \(n_{\text{components}}\) is the size of the projected subspace. By default the density of non zero elements is set to the minimum density as recommended by Ping Li et al.: \(1 / \sqrt{n_{\text{features}}}\).

Here a small excerpt which illustrates how to use the sparse random projection transformer:

```
>>> import numpy as np
>>> from sklearn import random_projection
>>> X = np.random.rand(100, 10000)
>>> transformer = random_projection.SparseRandomProjection()
>>> X_new = transformer.fit_transform(X)
>>> X_new.shape
(100, 3947)
```

## 6.6.4. Inverse Transform¶

The random projection transformers have `compute_inverse_components`

parameter. When
set to True, after creating the random `components_`

matrix during fitting,
the transformer computes the pseudo-inverse of this matrix and stores it as
`inverse_components_`

. The `inverse_components_`

matrix has shape
\(n_{features} \times n_{components}\), and it is always a dense matrix,
regardless of whether the components matrix is sparse or dense. So depending on
the number of features and components, it may use a lot of memory.

When the `inverse_transform`

method is called, it computes the product of the
input `X`

and the transpose of the inverse components. If the inverse components have
been computed during fit, they are reused at each call to `inverse_transform`

.
Otherwise they are recomputed each time, which can be costly. The result is always
dense, even if `X`

is sparse.

Here a small code example which illustrates how to use the inverse transform feature:

```
>>> import numpy as np
>>> from sklearn.random_projection import SparseRandomProjection
>>> X = np.random.rand(100, 10000)
>>> transformer = SparseRandomProjection(
... compute_inverse_components=True
... )
...
>>> X_new = transformer.fit_transform(X)
>>> X_new.shape
(100, 3947)
>>> X_new_inversed = transformer.inverse_transform(X_new)
>>> X_new_inversed.shape
(100, 10000)
>>> X_new_again = transformer.transform(X_new_inversed)
>>> np.allclose(X_new, X_new_again)
True
```