Boosting the kernelized shapelets: Theory and algorithms for local features
We consider binary classification problems using local features of objects.
One of motivating applications is time-series classification, where features reflecting
some local closeness measure between a time-series and a pattern sequence called
shapelet are useful. Despite the empirical success of such approaches using local
features, the generalization ability of resulting hypotheses is not fully understood
and previous work relies on a bunch of heuristics.
In this talk, we formulate a class of hypotheses using local features, where the richness of features is controlled by kernels.
We derive generalization bounds of sparse ensembles over the class which is exponentially better than a standard analysis in terms of the number
of possible local features. The resulting optimization problem is well suited to the
boosting approach and the weak learning problem is formulated as a DC program,
for which practical algorithms exist. In preliminary experiments on time-series
data sets, our method achieves competitive accuracy
with the state-of-the-art algorithms with small parameter-tuning cost.
This is a joint work with Daiki Suehiro, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, and Akiko Takeda.