  Portfolio optimization is one of the essential fields of focus in finance.
There has been an increasing demand for novel computational methods in this
area to compute portfolios with better returns and lower risks in recent years.
We present a novel computational method called Representation Portfolio
Selection (RPS) by redefining the distance matrix of financial assets using
Representation Learning and Clustering algorithms for portfolio selection to
increase diversification. RPS proposes a heuristic for getting closer to the
optimal subset of assets. Using empirical results in this paper, we demonstrate
that widely used portfolio optimization algorithms, such as MVO, CLA, and HRP,
can benefit from our asset subset selection.
