ABSTRACT
The concept of sparsity has attracted considerable interest in the field of machine learning in the past few years. Sparse feature vectors contain mostly values of zero and one or a few nonzero values. Although these feature vectors can be classified by traditional machine learning algorithms, such as Support Vector Machines (SVMs), there are various recently developed algorithms that explicitly take advantage of the sparse nature of the data, leading to massive speedups in time, as well as improved performance. Some fields that have benefited from the use of sparse algorithms are finance, bioinformatics, text mining [1], and image classification [4]. Because of their speed, these algorithms perform well on very large collections of data [2]; large collections are becoming increasingly relevant given the huge amounts of data collected and warehoused by Internet businesses.
