In machine learning, the data are usually represented in a high dimensional feature space. Nevertheless in practice, the data are restricted to a limited area of the feature space. This leads to the well known problem of the curse of dimensionality. The manifold learning techniques, also known as dimensionality reduction aim to find a mapping of the data from the high dimensional feature space to a new space of lower dimensions. The manifold learning methods estimates the geometry of the dataset locally, around each data point.