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Is Latent Dirichlet Allocation a form of clustering?
Why use LDA? If you view the number of topics as a number of clusters and the probabilities as the proportion of cluster membership, then using LDA is a way of soft-clustering your composites and parts. Contrast this with say, k-means, where each entity can only belong to one cluster (hard-clustering).
Is LDA a clustering method?
Strictly speaking, Latent Dirichlet Allocation (LDA) is not a clustering algorithm. That is, each document will be assigned to a single cluster. If, instead, we were to apply LDA to a set of documents, we would end up with a probability distribution of groupings—or topics—for each document.
What is the use of latent Dirichlet allocation?
In natural language processing, the Latent Dirichlet Allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
Is there a distance metric for LDA clustering?
LDA does not have a distance metric. The intuition behind the LDA topic model is that words belonging to a topic appear together in documents. Unlike typical clustering algorithms like K-Means, it does not assume any distance measure between topics.
How does the LDA topic model work for clustering?
The intuition behind the LDA topic model is that words belonging to a topic appear together in documents. Unlike typical clustering algorithms like K-Means, it does not assume any distance measure between topics. Instead it infers topics purely based on word counts, based on the bag-of-words representation of documents.
What is similarity / distance measure for LDA algorithm?
Since a similarity/distance measure is crucial for every clustering algorithm, I wonder what this measure is for LDA. Since LDA works on text as a bag-of-word model, can someone imagine the similarity between topics (clusters) are the representative words between those clusters?
Can you imagine the similarity between topics ( clusters )?
Since LDA works on text as a bag-of-word model, can someone imagine the similarity between topics (clusters) are the representative words between those clusters? If those are the representative words, is the measure for clustering those topics, the similarity between them in vector space?