1. A computer-implemented method for analyzing data containing a noise component, the method comprising: inputting the data into a computing environment comprising one or more pre-processing program modules and one or more support vector machine modules stored on a drive or a system memory of a computer or computer network; dividing the data into a training dataset and a test dataset; associating each datapoint within the training dataset with a tangent vector by applying a local transformation by the noise component to the datapoint; mapping the training dataset and the tangent vectors into feature space; in feature space, training a support vector machine comprising a kernel function to calculate a hyperplane for separating the training dataset into two or more classes, wherein the hyperplane has a normal vector that is orthogonal to the tangent vectors; testing the trained support vector machine using the test dataset to determine whether an optimal solution has been achieved; if the optimal solution has been achieved, inputting a new dataset having unknown classifications into the support vector machine; and generating an output comprising an identification of patterns identified in the new dataset to one or more of the system memory, the drive, an external memory, and a display device.