Robust Least Squares for fitting data (quadric surface)

Here I would like to show you some changes in the source code of the Robust least squares fitting required for a general quadric surface (fitting of the planar model was introduced in the previous post). Assume you want to fit a set of data points in the three-dimensional space with a general quadric described by five parameters \bf{a_{1}}, \bf{a_{2}}, \bf{a_{3}}, \bf{a_{4}}, and \bf{a_{5}} as a function of \bf{x} and \bf{y} in the following way:

\bf{z = a_{1}x^{2} + a_{2}y^{2} + a_{3}x + a_{4}y + a_{5}}.

Again, fisrt we predefine the surface model with the so-called “ground truth” coefficients which we will need for evaluation of the fitting data:

import matplotlib.pyplot as plt
import numpy as np

from planar_model import (ROBUST_LSQ_N, add_gaussian_noise, add_outliers,
                          build_z_vector, estimate_model)

# ground truth model coefficients
a_1, a_2, a_3, a_4, a_5 = 0.1, -0.2, -0.3, 0.1, 0.15
a_ground_truth = [a_1, a_2, a_3, a_4, a_5]
print(f'Ground truth model coefficients: {a_ground_truth}')

# create a coordinate matrix
n_x, n_y = np.linspace(-1, 1, 41), np.linspace(-1, 1, 41)
x_values, y_values = np.meshgrid(n_x, n_y)

# make the estimation
z_values = a_1 * x_values ** 2
z_values += a_2 * y_values ** 2
z_values += a_3 * x_values + a_4 * y_values + a_5

Then, similar to the case with the planar surface, we find the model coefficients and display the output of the found model for these three cases:

  • Input data is corrupted by Gaussian noise only and the Regular linear least squares method is used.
  • Input data is corrupted by Gaussian noise AND outliers, Regular linear least squares method is used.
  • Input data is corrupted by Gaussian noise AND outliers, Robust linear least squares method is used.

Note that in the current example we have 5 parameters which need to be estimated, below you can see required changes in the source code:

def build_x_matrix(x_values: np.ndarray, y_values: np.ndarray) -> np.ndarray:
    x_flatten, y_flatten = x_values.flatten(), y_values.flatten()
    z_ones = np.ones([x_values.size, 1])
    x_flatten2, y_flatten2 = x_flatten ** 2, y_flatten ** 2

    return np.hstack((np.reshape(x_flatten2, ([x_flatten2.size, 1])),
                      np.reshape(y_flatten2, ([y_flatten2.size, 1])),
                      np.reshape(x_flatten, ([x_flatten.size, 1])),
                      np.reshape(y_flatten, ([y_flatten.size, 1])),

def robust_lsq_step(x_matrix: np.ndarray, z_vector: np.ndarray,
                    a_robust_lsq: np.ndarray,
                    z_values: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
    # compute absolute value of residuals (fit minus data)
    abs_residuals = abs(x_matrix @ a_robust_lsq - z_vector)

    # compute the scaling factor for the standardization of residuals
    # using the median absolute deviation of the residuals
    # 6.9460 is a tuning constant (4.685/0.6745)
    abs_res_scale = 6.9460 * np.median(abs_residuals)

    # standardize residuals
    w = abs_residuals / abs_res_scale

    # compute the robust bisquare weights excluding outliers
    w[(w > 1).nonzero()] = 0

    # calculate robust weights for 'good' points; note that if you supply
    # your own regression weight vector, the final weight is the product of
    # the robust weight and the regression weight
    tmp = 1 - w[(w != 0).nonzero()] ** 2
    w[(w != 0).nonzero()] = tmp ** 2

    # get weighted x values
    x_weighted = np.tile(w, (1, 5)) * x_matrix

    a = x_weighted.T @ x_matrix
    b = x_weighted.T @ z_vector

    # get the least-squares solution to a linear matrix equation
    a_robust_lsq = np.linalg.lstsq(a, b, rcond=None)[0]
    z_result = x_matrix @ a_robust_lsq

    return np.reshape(z_result, z_values.shape), a_robust_lsq

def robust_least_squares_noise_outliers(x_values: np.ndarray,
                                        y_values: np.ndarray,
                                        z_values: np.ndarray) -> None:
    """Input data is corrupted by gaussian noise AND outliers,
    robust least squares method will be used"""
    # start with the least squares solution
    z_corrupted = add_gaussian_noise(z_values)
    z_corrupted = add_outliers(x_values, z_corrupted)

    x_matrix = build_x_matrix(x_values, y_values)
    z_vector = build_z_vector(z_corrupted)
    z_result, a_robust_lsq = estimate_model(x_matrix, z_corrupted)

    # iterate till the fit converges
    for _ in range(ROBUST_LSQ_N):
        z_result, a_robust_lsq = robust_lsq_step(
            x_matrix, z_vector, a_robust_lsq, z_values)

    print(f'Robust Least Squares (noise and outliers): {a_robust_lsq}')

    plt.figure(figsize=(10, 10))
    plt.title('Robust estimate (corrupted by noise AND outliers)')
    plt.imshow(np.hstack((z_values, z_corrupted, z_result)))
    plt.clim(np.min(z_values), np.max(z_values))
robust_least_squares_noise_outliers(x_values, y_values, z_values)

That’s it. Here you can see output results for all three cases:

And again the output produced by the Robust linear least squares method looks pretty good despite many outliers in the input data. The code shown above is available here.

Best wishes,

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