Package elki.math.linearalgebra.pca.weightfunctions
Weight functions used in weighted PCA via
WeightedCovarianceMatrixBuilder
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Interface Summary Interface Description WeightFunction WeightFunction interface that allows the use of various distance-based weight functions. -
Class Summary Class Description ConstantWeight Constant weight function.ErfcStddevWeight Gaussian Error Function Weight function, scaled using stddev using: erfc(1√2distanceσ).ErfcWeight Gaussian Error Function Weight function, scaled such that the result it 0.1 when the distance is the maximum using: erfc(1.1630871536766736distancemax.ExponentialStddevWeight Exponential Weight function, scaled using the standard deviation using: \sigma \exp(-\frac{1}{2} \frac{\text{distance}}{\sigma}) .ExponentialWeight Exponential Weight function, scaled such that the result it 0.1 at distance equal max, so it does not completely disappear using: \exp(-2.3025850929940455 \frac{\text{distance}}{\max})GaussStddevWeight Gaussian weight function, scaled using standard deviation \frac{1}{\sqrt{2\pi}} \exp(-\frac{\text{dist}^2}{2\sigma^2})GaussWeight Gaussian weight function, scaled such that the result it 0.1 when distance equals the maximum, using \exp(-2.3025850929940455 \frac{\text{dist}^2}{\max^2}) .InverseLinearWeight Inverse linear weight function using .1+0.9\frac{\text{distance}}{\max}.InverseProportionalStddevWeight Inverse proportional weight function, scaled using the standard deviation using: 1 / (1 + \frac{distance}{\sigma})InverseProportionalWeight Inverse proportional weight function, scaled using the maximum using: 1 / (1 + \frac{\text{distance}}{\max} )LinearWeight Linear weight function, scaled using the maximum such that it goes from 1.0 to 0.1 using: 1 - 0.9 \frac{\text{distance}}{\max}QuadraticStddevWeight Quadratic weight function, scaled using the standard deviation: \max\{0.0, 1.0 - \frac{\text{dist}^2}{3\sigma^2} \} .QuadraticWeight Quadratic weight function, scaled using the maximum to reach 0.1 at that point using: 1.0 - 0.9 \frac{\text{dist}^2}{\max^2}\}