Abstract
Existing methods on representation-based subspace
clustering mainly treat all features of data as a
whole to learn a single self-representation and get
one clustering solution. Real data however are often complex and consist of multiple attributes or
sub-features, such as a face image has expressions
or genders. Each attribute is distinct and complementary on depicting the data. Failing to explore
attributes and capture the complementary information among them may lead to an inaccurate representation. Moreover, a single clustering solution is
rather limited to depict data, which can often be interpreted from different aspects and grouped into
multiple clusters according to attributes. Therefore, we propose an innovative model called attributed subspace clustering (ASC). It simultaneously learns multiple self-representations on latent
representations derived from original data. By utilizing Hilbert Schmidt Independence Criterion as
a co-regularizing term, ASC enforces that each
self-representation is independent and corresponds
to a specific attribute. A more comprehensive
self-representation is then established by adding
these self-representations. Experiments on several
benchmark image datasets have demonstrated the
effectiveness of ASC not only in terms of clustering
accuracy achieved by the integrated representation,
but also the diverse interpretation of data, which is
beyond what current approaches can offer