  We propose a method that learns a discriminative yet semantic space for
object categorization, where we also embed auxiliary semantic entities such as
supercategories and attributes. Contrary to prior work which only utilized them
as side information, we explicitly embed the semantic entities into the same
space where we embed categories, which enables us to represent a category as
their linear combination. By exploiting such a unified model for semantics, we
enforce each category to be represented by a supercategory + sparse combination
of attributes, with an additional exclusive regularization to learn
discriminative composition.
