We reduce dimensions and create latent features internally every day. For example, people invent concepts like ‘coolness’, but we can’t directly measure how cool someone is. Other people exhibit different patterns of behavior, which we internally map or reduce to our one dimension of ‘coolness’. So coolness is an example of a latent feature in that it’s unobserved and not measurable directly, and we could think of it as reducing dimensions because perhaps it’s a combination of many ‘features’ we’ve observed about the person and implicitly weighted in our mind.
Two things are happening here: the dimensionality is reduced into a single feature and the latent aspect of that feature.
Quoted from: O’Neil, Cathy & Schutt, Rachel. “Doing Data Science”. O’Reilly. 2013.