1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
|
<object data="../img/pipeline-top-3.svg" type="image/svg+xml" id="pipeline-top">
</object>
# Transformations
Once data is [loaded](load_save.html) and any necessary
[preprocessing and feature extraction](preprocessing.md) is done,
one of mlpack's transformations can be used to transform data into a
new space.
*Note: this section is under construction and not all functionality is
documented yet.*
## Matrix decompositions
Decompose a matrix into two or more components.
* [AMF](methods/amf.md): alternating matrix factorization
* [NMF](methods/nmf.md): non-negative matrix factorization
## Linear transformations
Linearly map a matrix onto a new basis, optionally performing dimensionality
reduction.
* [PCA](methods/pca.md): principal components analysis
* [RADICAL](methods/radical.md): an independent components analysis technique
## Metric learning techniques
Learn a [distance metric](core/distances.md) based on a data matrix.
* [LMNN](methods/lmnn.md): large margin nearest neighbor
* [NCA](methods/nca.md): neighborhood components analysis
## Coding techniques
Encode data points in a matrix as a combination of points in a dictionary.
* [LocalCoordinateCoding](methods/local_coordinate_coding.md): local coordinate
coding with dictionary learning
* [SparseCoding](methods/sparse_coding.md): sparse coding with dictionary
learning
|
