Feature selection for classification using neighborhood component analysis (NCA)

`FeatureSelectionNCAClassification`

object contains the data, fitting
information, feature weights, and other parameters of a neighborhood component analysis
(NCA) model. `fscnca`

learns the feature weights using a
diagonal adaptation of NCA and returns an instance of a
`FeatureSelectionNCAClassification`

object. The function achieves
feature selection by regularizing the feature weights.

Create a `FeatureSelectionNCAClassification`

object using `fscnca`

.

`NumObservations`

— Number of observations in the training datascalar

Number of observations in the training data (`X`

and `Y`

)
after removing `NaN`

or `Inf`

values,
stored as a scalar.

**Data Types: **`double`

`ModelParameters`

— Model parametersstructure

Model parameters used for training the model, stored as a structure.

You can access the fields of `ModelParameters`

using
dot notation.

For example, for a FeatureSelectionNCAClassification object named `mdl`

,
you can access the `LossFunction`

value using `mdl.ModelParameters.LossFunction`

.

**Data Types: **`struct`

`Lambda`

— Regularization parameterscalar

Regularization parameter used for training this model, stored
as a scalar. For *n* observations, the best `Lambda`

value
that minimizes the generalization error of the NCA model is expected
to be a multiple of 1/*n*.

**Data Types: **`double`

`FitMethod`

— Name of fitting method`'exact'`

| `'none'`

| `'average'`

Name of the fitting method used to fit this model, stored as one of the following:

`'exact'`

— Perform fitting using all of the data.`'none'`

— No fitting. Use this option to evaluate the generalization error of the NCA model using the initial feature weights supplied in the call to`fscnca`

.`'average'`

— Divide the data into partitions (subsets), fit each partition using the`exact`

method, and return the average of the feature weights. You can specify the number of partitions using the`NumPartitions`

name-value pair argument.

`Solver`

— Name of the solver used to fit this model`'lbfgs'`

| `'sgd'`

| `'minibatch-lbfgs'`

Name of the solver used to fit this model, stored as one of the following:

`'lbfgs'`

— Limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm`'sgd'`

— Stochastic gradient descent (SGD) algorithm`'minibatch-lbfgs'`

— stochastic gradient descent with LBFGS algorithm applied to mini-batches

`GradientTolerance`

— Relative convergence tolerance on gradient normpositive scalar

Relative convergence tolerance on the gradient norm for the `'lbfgs'`

and `'minibatch-lbfgs'`

solvers,
stored as a positive scalar value.

**Data Types: **`double`

`IterationLimit`

— Maximum number of iterations for optimizationpositive integer

Maximum number of iterations for optimization, stored as a positive integer value.

**Data Types: **`double`

`PassLimit`

— Maximum number of passespositive integer

Maximum number of passes for `'sgd'`

and `'minibatch-lbfgs'`

solvers. Every
pass processes all of the observations in the data.

**Data Types: **`double`

`InitialLearningRate`

— Initial learning ratepositive real scalar

Initial learning rate for the `'sgd'`

and
`'minibatch-lbfgs'`

solvers, stored as a positive real
scalar. The
learning rate decays over iterations starting at the value specified for
`InitialLearningRate`

.

Use the `NumTuningIterations`

and
`TuningSubsetSize`

name-value pair arguments to
control the automatic tuning of initial learning rate in the call to
`fscnca`

.

**Data Types: **`double`

`Verbose`

— Verbosity level indicatornonnegative integer

Verbosity level indicator, stored as a nonnegative integer. Possible values are:

0 — No convergence summary

1 — Convergence summary, including norm of gradient and objective function value

>1 — More convergence information, depending on the fitting algorithm. When you use the

`'minibatch-lbfgs'`

solver and verbosity level > 1, the convergence information includes the iteration log from intermediate minibatch LBFGS fits.

**Data Types: **`double`

`InitialFeatureWeights`

— Initial feature weightsInitial feature weights, stored as a *p*-by-1
vector of positive real scalars, where *p* is the
number of predictors in `X`

.

**Data Types: **`double`

`FeatureWeights`

— Feature weightsFeature weights, stored as a *p*-by-1 vector of real
scalars, where *p* is the number of predictors in
`X`

.

If `FitMethod`

is `'average'`

, then
`FeatureWeights`

is a
*p*-by-*m* matrix.
*m* is the number of partitions specified via the
`'NumPartitions'`

name-value pair argument in the call
to `fscnca`

.

The absolute value of `FeatureWeights(k)`

is a measure of
the importance of predictor `k`

. A
`FeatureWeights(k)`

value that is close to 0 indicates
that predictor `k`

does not influence the response in
`Y`

.

**Data Types: **`double`

`FitInfo`

— Fit informationstructure

Fit information, stored as a structure with the following fields.

Field Name | Meaning |
---|---|

`Iteration` | Iteration index |

`Objective` | Regularized objective function for minimization |

`UnregularizedObjective` | Unregularized objective function for minimization |

`Gradient` | Gradient of regularized objective function for minimization |

For classification,

`UnregularizedObjective`

represents the negative of the leave-one-out accuracy of the NCA classifier on the training data.For regression,

`UnregularizedObjective`

represents the leave-one-out loss between the true response and the predicted response when using the NCA regression model.For the

`'lbfgs'`

solver,`Gradient`

is the final gradient. For the`'sgd'`

and`'minibatch-lbfgs'`

solvers,`Gradient`

is the final mini-batch gradient.If

`FitMethod`

is`'average'`

, then`FitInfo`

is an*m*-by-1 structure array, where*m*is the number of partitions specified via the`'NumPartitions'`

name-value pair argument.

You can access the fields of `FitInfo`

using
dot notation. For example, for a FeatureSelectionNCAClassificationobject named `mdl`

,
you can access the `Objective`

field using `mdl.FitInfo.Objective`

.

**Data Types: **`struct`

`Mu`

— Predictor means`[]`

Predictor means, stored as a *p*-by-1 vector
for standardized training data. In this case, the `predict`

method
centers predictor matrix `X`

by subtracting the
respective element of `Mu`

from every column.

If data is not standardized during training, then `Mu`

is
empty.

**Data Types: **`double`

`Sigma`

— Predictor standard deviations`[]`

Predictor standard deviations, stored as a *p*-by-1
vector for standardized training data. In this case, the `predict`

method
scales predictor matrix `X`

by dividing every column
by the respective element of `Sigma`

after centering
the data using `Mu`

.

If data is not standardized during training, then `Sigma`

is
empty.

**Data Types: **`double`

`X`

— Predictor valuesPredictor values used to train this model, stored as an *n*-by-*p* matrix. *n* is
the number of observations and *p* is the number
of predictor variables in the training data.

**Data Types: **`double`

`Y`

— Response valuesnumeric vector of size

Response values used to train this model, stored as a numeric
vector of size *n*, where n is the number of observations.

**Data Types: **`double`

`W`

— Observation weightsnumeric vector of size

Observation weights used to train this model, stored as a numeric
vector of size *n*. The sum of observation weights
is *n*.

**Data Types: **`double`

loss | Evaluate accuracy of learned feature weights on test data |

predict | Predict responses using neighborhood component analysis (NCA) classifier |

refit | Refit neighborhood component analysis (NCA) model for classification |

`FeatureSelectionNCAClassification`

ObjectLoad the sample data.

`load ionosphere`

The data set has 34 continuous predictors. The response variable is the radar returns, labeled as b (bad) or g (good).

Fit a neighborhood component analysis (NCA) model for classification to detect the relevant features.

mdl = fscnca(X,Y);

The returned NCA model, `mdl`

, is a `FeatureSelectionNCAClassification`

object. This object stores information about the training data, model, and optimization. You can access the object properties, such as the feature weights, using dot notation.

Plot the feature weights.

figure() plot(mdl.FeatureWeights,'ro') xlabel('Feature Index') ylabel('Feature Weight') grid on

The weights of the irrelevant features are zero. The `'Verbose',1`

option in the call to `fscnca`

displays the optimization information on the command line. You can also visualize the optimization process by plotting the objective function versus the iteration number.

figure plot(mdl.FitInfo.Iteration,mdl.FitInfo.Objective,'ro-') grid on xlabel('Iteration Number') ylabel('Objective')

The `ModelParameters`

property is a `struct`

that contains more information about the model. You can access the fields of this property using dot notation. For example, see if the data was standardized or not.

mdl.ModelParameters.Standardize

`ans = `*logical*
0

`0`

means that the data was not standardized before fitting the NCA model. You can standardize the predictors when they are on very different scales using the `'Standardize',1`

name-value pair argument in the call to `fscnca`

.

Value. To learn how value classes affect copy operations, see Copying Objects (MATLAB).

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