Search code examples
machine-learningclassificationsupervised-learning

Given a feature vector, how to find whether my data points are linearly separable

I have a feature vector in matrix notation, and I have data points in 2D plane. How to find whether my data points are linearly separable with that feature vector?

One can check whether there exists a line divides the data points into two. If there isn't a line, how to check for linear separability in higher dimensions?

Solution

  • A theoretical answer

    If we assume the samples of the two classes are distributed according to a Gaussian, we will get a quadratic function describing the decision boundary in the general case.

    If the covariance matrices are identical we get a linear decision boundary.

    A practical answer

    See the SO discussion here.