  Hyperalignment has been widely employed in Multivariate Pattern (MVP)
analysis to discover the cognitive states in the human brains based on
multi-subject functional Magnetic Resonance Imaging (fMRI) datasets. Most of
the existing HA methods utilized unsupervised approaches, where they only
maximized the correlation between the voxels with the same position in the time
series. However, these unsupervised solutions may not be optimum for handling
the functional alignment in the supervised MVP problems. This paper proposes a
Supervised Hyperalignment (SHA) method to ensure better functional alignment
for MVP analysis, where the proposed method provides a supervised shared space
that can maximize the correlation among the stimuli belonging to the same
category and minimize the correlation between distinct categories of stimuli.
Further, SHA employs a generalized optimization solution, which generates the
shared space and calculates the mapped features in a single iteration, hence
with optimum time and space complexities for large datasets. Experiments on
multi-subject datasets demonstrate that SHA method achieves up to 19% better
performance for multi-class problems over the state-of-the-art HA algorithms.
