Meaning of latent features?
At the expense of over-simplication, latent features are 'hidden' features to distinguish them from observed features. Latent features are computed from observed features using matrix factorization. An example would be text document analysis. 'words' extracted from the documents are features. If you factorize the data of words you can find 'topics', where 'topic' is a group of words with semantic relevance. Low-rank matrix factorization maps several rows (observed features) to a smaller set of rows (latent features).
To elaborate, the document could have observed features (words) like [sail-boat, schooner, yatch, steamer, cruiser] which would 'factorize' to latent feature (topic) like 'ship' and 'boat'. 

[sail-boat, schooner, yatch, steamer, cruiser, ...] -> [ship, boat]

The underlying idea is that latent features are semantically relevant 'aggregates' of observered features. When you have large-scale, high-dimensional, and noisy observered features, it makes sense to build your classifier on latent features.

This is a of course a simplified description to elucidate the concept. You can read the details on Latent Dirichlet Allocation (LDA) or probabilistic Latent Semantic Analysis (pLSA) models for an accurate description.