This paper formulates a generalized classification algorithm with an application to classifying (or `decoding') neural activity in the brain. Medical doctors and researchers have long been interested in how brain activity correlates to body movement. Experiments have been conducted on patients whom are unable to move, in order to gain insight as to how thinking about movements might generate discernable neural activity. Researchers are tasked with determining which neurons are responsible for different imagined movements and how the firing behavior changes, given neural firing data. For instance, imagined movements may include wrist flexion, elbow extension, or closing the hand. This is just one of many applications to data classification. Though this article deals with an application in neuroscience, the generalized algorithm proposed in this article has applications in scientific areas ranging from neuroscience to acoustic and medical imaging.