  Extremely high data rates expected in next-generation radio interferometers
necessitate a fast and robust way to process measurements in a big data
context. Dimensionality reduction can alleviate computational load needed to
process these data, in terms of both computing speed and memory usage. In this
article, we present image reconstruction results from highly reduced
radio-interferometric data, following our previously proposed data
dimensionality reduction method, $\mathrm{R}_{\mathrm{sing}}$, based on
studying the distribution of the singular values of the measurement operator.
This method comprises a simple weighted, subsampled discrete Fourier transform
of the dirty image. Additionally, we show that an alternative gridding-based
reduction method works well for target data sizes of the same order as the
image size. We reconstruct images from well-calibrated VLA data to showcase the
robustness of our proposed method down to very low data sizes in a 'real data'
setting. We show through comparisons with the conventional reduction method of
time- and frequency-averaging, that our proposed method produces more accurate
reconstructions while reducing data size much further, and is particularly
robust when data sizes are aggressively reduced to low fractions of the image
size. $\mathrm{R}_{\mathrm{sing}}$ can function in a block-wise fashion, and
could be used in the future to process incoming data by blocks in real-time,
thus opening up the possibility of performing 'on-line' imaging as the data are
being acquired. MATLAB code for the proposed dimensionality reduction method is
available on GitHub.
