  We introduce the manifold of {\it restricted} $n\times n$ positive
semidefinite matrices of fixed rank $p$, denoted $S(n,p)^{*}$. The manifold
itself is an open and dense submanifold of $S(n,p)$, the manifold of $n\times
n$ positive semidefinite matrices of the same rank $p$, when both are viewed as
manifolds in $\mathbb{R}^{n\times n}$. This density is the key fact that makes
the consideration of $S(n,p)^{*}$ statistically meaningful. We furnish
$S(n,p)^{*}$ with a convenient, and geodesically complete, Riemannian geometry,
as well as a Lie group structure, that permits analytical closed forms for
endpoint geodesics, parallel transports, Fr\'echet means, exponential and
logarithmic maps. This task is done partly through utilizing a {\it reduced}
Cholesky decomposition, whose algorithm is also provided. We produce a second
algorithm from this framework to estimate principal eigenspaces and demonstrate
its superior performance over other existing algorithms.
