There are several other feature-selection methods that you will discover while reading through machine learning literature. Some don't even look like they are feature selection methods because they are embedded in the learning process (not to be confused with the aforementioned wrappers). Decision trees, for instance, have a feature selection mechanism implanted deep in their core. Other learning methods employ some kind of regularization that punishes model complexity, hence driving the learning process toward good performing models that are still simple. They do this by decreasing the less impactful features' importance to zero and then dropping them (L1 regularization).

Often, the power of machine learning methods has to be attributed to their implanted feature selection methods to a great degree.