  Aims: This study aims to trace the chronological evolution of galaxy spectra
over cosmic time. Focusing on the VIPERS dataset, we seek to understand the
diverse population of galaxies within narrow redshift bins, comparing our
findings with the previously mapped diversity of SDSS galaxies.
  Methods: We use Fisher-EM, an unsupervised subspace model-based
classification algorithm to classify a dataset of 79,224 spectra from the
VIPERS. The dataset was divided into 26 samples by bins of redshift ranging
from 0.4 - 1.2, which were classified independently. Classes of subsequent bins
were linked through the k-Nearest Neighbour method to create a chronological
tree of classes at different epochs.
  Results: Based on the optical spectra, three main chronological galaxy
branches have emerged: (i) red passive, (ii) blue star-forming, and (iii) very
blue, possibly associated with AGN activity. Each of the branches
differentiates into sub-branches discriminating finer properties such as D4000
break, colour, star-formation rate, and stellar masses and/or disappear with
cosmic time. Notably, these classes align remarkably well with the branches
identified in a previous SDSS analysis, indicating a robust and consistent
classification across datasets. The chronological "tree" constructed from
VIPERS data provides valuable insights into the temporal evolution of these
spectral classes.
  Conclusions: The synergy between VIPERS and SDSS datasets enhances our
understanding of the evolutionary pathways of galaxy spectra. The remarkable
correspondence between independently derived branches in both datasets
underscores the reliability of our unsupervised machine-learning approach. The
three sub-trees show complex branching structures highlighting different
physical and evolutionary behaviours. This study contributes to the broader
comprehension of galaxy evolution.
