Nowadays, accessing data is easier and cheaper than ever before. This has led to the proliferation of data in organizations' data warehouses and on the Internet. Analyzing this data is not a trivial task, as its quantity often makes analysis difficult or unpractical. For instance, the data is often more abundant than available memory on the machines. The available computational power is also often not enough to analyze the data in a reasonable time frame. One solution is to have recourse to technologies that deal with high dimensionality in data (Big Data). These solutions typically use the memory and computing power of several machines for analysis (computer clusters). But most organizations do not have such an infrastructure. Therefore, a more practical solution is to reduce the dimensionality of the data while keeping the essential information intact.
Another reason to reduce dimensionality is that, in some cases, there...