Removing NaN values from measurement table before computing `feature_correlation_matrix`

[marabuuu]

Currently, before computing the feature_correlation_matrix all rows with NaN values are removed (see documentation and code) https://github.com/haesleinhuepf/napari-accelerated-pixel-and-object-classification/blob/68da4c98e34a5a5a41e401affd3c41f03d5e6a33/napari_accelerated_pixel_and_object_classification/_custom_table_row_classifier.py#L264 However, in my case I had the measurement moments_normalized in my table. This measurement had NaN values for each label (see screenshot). This means, all rows were removed and the feature_correlation_matrix only contained NaN values. Maybe in some cases, it would make more sense to remove the column and not the row. But I am not sure if this would still be good scientific practice.

Best, Mara