Abstract- Kernel trick and projection to tangent spaces are two choices for linearizing the data points lying on Riemannian manifolds. These approaches are used to provide the prerequisites for applying standard machine learning methods on Riemannian manifolds. Classical kernels implicitly project data to high dimensional feature space without considering the intrinsic geometry of data points. ...

In recent years, production of text documents has seen an exponential growth, which is the reason why their proper classification seems necessary for better access. One of the main problems of classifying text documents is working in high-dimensional feature space. Feature Selection (FS) is one of the ways to reduce the number of text attributes. So, working with a great bulk of the feature spa...

Detecting faces in cluttered backgrounds and real world has remained as an unsolved problem yet. In this paper, by using composition of some kind of independent features and one of the most common appearance based approaches, and multilayered perceptron (MLP) neural networks, not only some questions have been answered, but also the designed system achieved better performance rather than the pre...

abstract— due to the daily mass production and the widespread variation of medical x-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. in this paper, a medical x-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is prop...

Multispectral and hyperspectral feature spaces are useful for a variety of remote sensing applications ranging from spectral mixture modeling to discrete thematic classification. In many these applications, models used project the higher dimensional continuum reflectances (or radiances) onto lower mappings image target’s physical properties or categorical composition. such cases, characterizati...