appearance as in , then $\indfao_{n} = \{k: n \in A_{k}\}$. By definition of a feature allocation, $\indfao_{n}$ must have finite cardinality. The order-of-appearance labeling gives $
\indfao_{1} = \emptyset,
\indfao_{2} = \{1\},
\indfao_{3} = \{2,3,4\},
\indfao_{4} = \{1,3,5\},
\indfao_{5} = \{1\},
\indfao_{6} = \{5\}.
$

Order-of-appearance labeling is well-suited for matrix representations of feature allocations. The rows of the matrix correspond to indices $n$ and the columns correspond to features with order-of-appearance labels $k$. The matrix representation of the order-of-appearance labeling and resulting feature assignments $(\indfao_{n})$ for $n \in [6]$ is depicted in .

Note that when the feature allocation is a partition, there is exactly one feature containing any $m$, so this scheme reduces to the order-of-appearance scheme for cluster labeling.

Consider an exchangeable feature allocation $\randfa_{\infty}$. Give order-of-appearance labels to the features of this allocation, and let $\indfao_{n}$ be the set of feature labels for features containing $n$. So $\indfao_{n}$ is a random finite subset of $\mathbb{N}$. It can be thought of as a simple point process on $\mathbb{N}$; a discussion of measurability of such processes may be found in @kallenberg:2002:foundations [p. 178]. Our process is even simpler than a simple point process as it is globally 