# Classifying vectors that only contains 1001110101 numbers - Is Support Vector Machine the solution?

Assume that you are given a vector 0b1101001001011001 etc. And you are going to classify it. One can use Support Vector Machine, but is that method good for classifying data that are spare and binary?

Assume that you are given a real vector $$x$$ with dimension $$N$$. The vector contains only ones and zeros. $$x = [0, 1, 0, 0, 0, 1, 1]^T$$

You are not given one vector $$x$$, instead you are given a matrix of vectors e.g $$X = [x_1, x_2, x_3, \dots , x_M]$$ where $$N$$ is constant but $$M$$ can vary.

From the matrix $$X$$, you are also given a vector called labels $$y$$. The labels are labled either with -1 or +1. The labels $$y$$ is one vector with the same length as $$M$$.

So, from the labels $$y$$ and matrix $$X$$, you can find weights $$W$$ and bias $$b$$ on the form

$$y_i = Wx_i^T + b$$

But is SVM a good way to classify this type data?

• You need much more information than this to answer the question. Oct 26, 2023 at 8:40
• @PålGD done.... Oct 26, 2023 at 10:43