In very large-scale sparse settings, the optimization strategy is tightly coupled to the modeling, not orthogonal to it. The reason why is that "data beats algorithm": meaning that a "dumb" model that can be trained on…
Not quite - it's different from the kernel trick, which is impossible at this scale (there'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…
In very large-scale sparse settings, the optimization strategy is tightly coupled to the modeling, not orthogonal to it. The reason why is that "data beats algorithm": meaning that a "dumb" model that can be trained on…
Not quite - it's different from the kernel trick, which is impossible at this scale (there'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…