  We propose a Multimodal Machine Learning method for estimating the
Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long
been the subject of many investigations. Our method includes two main models,
i.e. the feature transformation model by multimodal representation learning,
and the photometric redshift estimation model by multimodal transfer learning.
The prediction accuracy of the photometric redshift was significantly improved
owing to the large amount of information offered by the generated spectral
features learned from photometric data via the MML. A total of 415,930 quasars
from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1
and 5, were screened for our experiments. We used |{\Delta}z| =
|(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and
demonstrated a 4.04% increase in accuracy. With the help of the generated
spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach
84.45% of the total test samples, whereas it reaches 80.41% for single-modal
photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown
to decreases from 0.1332 to 0.1235. Our method has the potential to be
generalized to other astronomical data analyses such as galaxy classification
and redshift prediction. The algorithm code can be found at
https://github.com/HongShuxin/PhotoRedshift-MML .
