Build the Projection Matrix To do so, we need eigenVectors and the number of columns of the projected data which is the number of outputs (principle components) used to transform the data.

Declaration

public static double[, ] GetProjectionMatrix(double[, ] eigenVector, int numberOfOutputs)

Parameters

Type	Name	Description
Double[,]	eigenVector
Int32	numberOfOutputs

Returns

Type	Description
Double[,]

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NoiseReduction(Double[,])

10-percentile Noise Reduction.

Declaration

public static double[, ] NoiseReduction(double[, ] matrix)

Parameters

Type	Name	Description
Double[,]	matrix

Returns

Type	Description
Double[,]

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ReconstructSpectrogram(Double[,], Double[,], Double[,], Int32)

reconstruct the spectrogram using sequential patches and the projection matrix.

Declaration

public static double[, ] ReconstructSpectrogram(double[, ] projectionMatrix, double[, ] sequentialPatchMatrix, double[, ] eigenVectors, int numberOfComponents)

Parameters

Type	Name	Description
Double[,]	projectionMatrix
Double[,]	sequentialPatchMatrix
Double[,]	eigenVectors
Int32	numberOfComponents

Returns

Type	Description
Double[,]

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Revert(Double[,], Double[,], Int32)

revert a set of projected data into its original space the output of the "Revert(Double[][])" method in Accord did not make sense. however, we use its API to do so.

Declaration

public static double[, ] Revert(double[, ] projectedData, double[, ] eigenVectors, int numberOfComponents)

Parameters

Type	Name	Description
Double[,]	projectedData
Double[,]	eigenVectors
Int32	numberOfComponents

Returns

Type	Description
Double[,]

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Whitening(Boolean, Double[,])

Declaration

public static PcaWhitening.Output Whitening(bool doWhitening, double[, ] matrix)

Parameters

Type	Name	Description
Boolean	doWhitening
Double[,]	matrix

Returns

Type	Description
PcaWhitening.Output