Latent dirichlet allocation Article Swipe
YOU?
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· 2003
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· DOI: https://doi.org/10.5555/944919.944937
· OA: W1880262756
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.
where every whisper of a word finds its place,
drawing lines between hearts and minds,
like the infinite dance of probabilities,
where topics swirl in a symphony of meaning.
Connection emerges from the chaos,
like a garden blooming in the cracks,
inspired by the beauty of shared narratives,
transforming silence into stories,
elevating voices once lost in the noise.
In every document, a pulse of intent,
each classification a step toward understanding,
collaborative filtering unearthing the gems,
as we sift through the sands of time,
seeking the truths that resonate within.
Connection, a bridge across divides,
inviting us to explore the unseen,
reminding us that every fragment matters,
that within the infinite mixture, we find our way,
we find our way back to… 🔁
