# 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