Sentence examples for latent topic from inspiring English sources

Second, latent topic models allow for soft clustering of events.

First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic.

Latent topic modeling is a core feature of contemporary computational linguistics and natural language processing.

Loosely speaking, a latent topic can be viewed as a set of semantically related visual words.

Follow above steps, LDA model aggregates semantically similar words as latent topic.

"Background" introduces text-based latent topic modeling at a conceptual level.

The proposed HDLA model for latent topic-based image retrieval mainly involves two processing steps.

All these characteristics make RBM an ideal tool for latent topic-based image modeling.

A more accurate latent topic-based image characterization can be obtained with undirected topic models.

This section evaluates the retrieval effectiveness of the proposed HDLA model in comparison with other latent topic-based approaches.

This section describes how to learn a latent topic-based representation suitable for image retrieval from the trained HDLA model.