Not quite - it&#x27;s different from the kernel trick, which is impossible at this scale (there&#x27;s no way you can train an RBF kernel in a decent amount of time when your space has 10^8 features and your training set has 10^9 observations). The idea of factorization machine is to <i>learn</i> the polynomial kernel, and it provides a mechanism to do so which scales &quot;linearly&quot; (with some arbitrary constant that you&#x27;re in control of).