  Multiview assisted learning has gained significant attention in recent years
in supervised learning genre. Availability of high performance computing
devices enables learning algorithms to search simultaneously over multiple
views or feature spaces to obtain an optimum classification performance. The
paper is a pioneering attempt of formulating a mathematical foundation for
realizing a multiview aided collaborative boosting architecture for multiclass
classification. Most of the present algorithms apply multiview learning
heuristically without exploring the fundamental mathematical changes imposed on
traditional boosting. Also, most of the algorithms are restricted to two class
or view setting. Our proposed mathematical framework enables collaborative
boosting across any finite dimensional view spaces for multiclass learning. The
boosting framework is based on forward stagewise additive model which minimizes
a novel exponential loss function. We show that the exponential loss function
essentially captures difficulty of a training sample space instead of the
traditional `1/0' loss. The new algorithm restricts a weak view from over
learning and thereby preventing overfitting. The model is inspired by our
earlier attempt on collaborative boosting which was devoid of mathematical
justification. The proposed algorithm is shown to converge much nearer to
global minimum in the exponential loss space and thus supersedes our previous
algorithm. The paper also presents analytical and numerical analysis of
convergence and margin bounds for multiview boosting algorithms and we show
that our proposed ensemble learning manifests lower error bound and higher
margin compared to our previous model. Also, the proposed model is compared
with traditional boosting and recent multiview boosting algorithms.
