Principal Component Analysis.
Attention: Available only with Activate commercial edition.
Syntax
parameters = pcafit(X)
parameters = pcafit(X,options)
Inputs
 X
 Training data.
 Type: double
 Dimension: vector  matrix
 options
 Type: struct

 n_components
 Number of components to keep. If n_components is not set, all components are kept: min(n_samples, n_features).
 If n_components is between 0 and 1 (exclusive) and svd_solver = 'full', number of components is selected such that the amount of variance that needs to be explained is greater than the percentage specified by n_components.
 If svd_solver = 'arpack', the number of components must be strictly less than the min(n_samples, n_features). So n_components will be min(n_samples, n_features) – 1.
 Type: integer
 Dimension: scalar
 svd_solver
 'auto' (default): solver is selected by a default policy based on dimension of X and n_components. If the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient 'randomized' method is enabled. Otherwise exact full SVD is computed and optionally truncated afterwards.
 'full': runs exact full SVD calling the standard LAPACK solver and selects the components by postprocessing.
 'arpack': runs SVD truncated to n_components calling ARPACK solver. It requires strictly that n_components is between 0 and min(n_samples, n_features).
 'randomized': runs randomized SVD by the method of Halko et al.
 Type: char
 Dimension: string
 tol
 Tolerance for singular values computed by svd_solver = 'arpack' (default: 0).
 Type: double
 Dimension: scalar
 iterated_power
 Number of iterations for the power method computed by svd_solver = 'randomized'. Must be of range [0, infinity). If not set, it is computed automatically.
 Type: integer
 Dimension: scalar
 random_state
 Determines random number generation for svd_solver = 'arpack' or 'randomized'.
 Type: integer
 Dimension: scalar
Outputs
 parameters
 Contains all the values passed to pcafit method as options. Additionally it has below keyvalue pairs.
 Type: struct

 components
 Principal axes in feature space, representing the directions of maximum variance in the data. The components are sorted by explained_variance.
 Type: double
 Dimension: matrix
 explained_variance
 Amount of variance explained by each of the selected components. It is equal to n_components largest eigenvalues of the covariance matrix of X.
 Type: double
 Dimension: vector
 explained_variance_ratio
 Percentage of variance explained by each of the selected components. If n_components is not set, then all components are stored and the sum of the ratios is equal to 1.0.
 Type: double
 Dimension: vector
 singular_values
 The singular values corresponding to each of the selected components. The singular values are equal to the 2norms of the n_components variable in the lowerdimensional space.
 Type: double
 Dimension: vector
 mean
 Per feature empirical mean, estimated from the training set.
 Type: double
 Dimension: vector
 n_components
 Estimated number of components when, n_components is set between 0 and 1 while fitting with svd_solver = 'full'.
 Type: integer
 Dimension: scalar
Example
Usage of pcafit with options
X = [1, 1; 2, 1; 3, 2; 1, 1; 2, 1; 3, 2];
options = struct;
options.n_components = 2;
parameters = pcafit(X, options);
> parameters
parameters = struct [
components: [Matrix] 2 x 2
0.83849 0.54491
0.54491 0.83849
explained_variance: [Matrix] 1 x 2
7.93954 0.06046
explained_variance_ratio: [Matrix] 1 x 2
0.99244 0.00756
]
Comments
Output 'parameters' should be passed as input to pcatransform function.