An Analysis of Heterogeneous Data with Extreme Learning via Unsupervised Multiple Kernels

Multiple sources without information about labels collect a large amount of data, often containing heterogeneous data, namely various types, structures as well as distributions. Such data can include Instagram, Twitter, and Facebook and YouTube, texts, images and videos. Advanced unsupervised learning methods (with multiple kernels) cabs are applied to extract information from such great unlabeled heterogeneous data MKL Traditional MKL algorithms are a good way to reveal information from multiple sources. Some efforts in managing difficult, heterogeneous distributed data, for example using the kernel, have currently been made to efficiently capture data from heterogeneous data. In recent times some unsupervised learning efforts have been made. In this paper, we illustrate the problem by heterogeneous knowledge and discuss various kernel and extreme learning approaches and their problems without supervision.