  In the current era of time-domain astronomy, it is increasingly important to
have rigorous, data driven models for classifying transients, including
supernovae. We present the first application of Principal Component Analysis to
the spectra of stripped-envelope core-collapse supernovae. We use one of the
largest compiled optical datasets of stripped-envelope supernovae, containing
160 SNe and 1551 spectra. We find that the first 5 principal components capture
79\% of the variance of our spectral sample, which contains the main families
of stripped supernovae: Ib, IIb, Ic and broad-lined Ic. We develop a
quantitative, data-driven classification method using a support vector machine,
and explore stripped-envelope supernovae classification as a function of phase
relative to V-band maximum light. Our classification method naturally
identifies "transition" supernovae and supernovae with contested labels, which
we discuss in detail. We find that the stripped-envelope supernovae types are
most distinguishable in the later phase ranges of $10\pm5$ days and $15\pm5$
days relative to V-band maximum, and we discuss the implications of our
findings for current and future surveys such as ZTF and LSST.
