  Sparse coding algorithm is an learning algorithm mainly for unsupervised
feature for finding succinct, a little above high - level Representation of
inputs, and it has successfully given a way for Deep learning. Our objective is
to use High - Level Representation data in form of unlabeled category to help
unsupervised learning task. when compared with labeled data, unlabeled data is
easier to acquire because, unlike labeled data it does not follow some
particular class labels. This really makes the Deep learning wider and
applicable to practical problems and learning. The main problem with sparse
coding is it uses Quadratic loss function and Gaussian noise mode. So, its
performs is very poor when binary or integer value or other Non- Gaussian type
data is applied. Thus first we propose an algorithm for solving the L1 -
regularized convex optimization algorithm for the problem to allow High - Level
Representation of unlabeled data. Through this we derive a optimal solution for
describing an approach to Deep learning algorithm by using sparse code.
