# 잠재 디리클레 할당

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## 노트

### 위키데이터

- ID : Q269236

### 말뭉치

- The Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is an unsupervised learning algorithm that attempts to describe a set of observations as a mixture of distinct categories.
^{[1]} - LDA is most commonly used to discover a user-specified number of topics shared by documents within a text corpus.
^{[1]} - This allows LDA to discover these word groups and use them to form topics.
^{[1]} - LDA expects data to be provided on the train channel, and optionally supports a test channel, which is scored by the final model.
^{[1]} - LDA is based on a bayesian probabilistic model where each topic has a discrete probability distribution of words and each document is composed of a mixture of topics.
^{[2]} - In this report, we first describe the mechanism of Latent Dirichlet Allocation.
^{[2]} - We then use two methods to implement LDA: Variational Inference and Collapsed Gibbs Sampling.
^{[2]} - LDA is a generative model, so it’s able to produce new documents.
^{[3]} - LDA will produce a distribution of topics over words.
^{[3]} - We also learned about LDA, what are Dirichlet distributions, how to generate new documents and find what each topic represents.
^{[3]} - The second approach, called latent Dirichlet allocation (LDA), uses a Bayesian approach to modeling documents and their corresponding topics and terms.
^{[4]} - LDA uses Bayesian methods in order to model each document as a mixture of topics and each topic as a mixture of words.
^{[4]} - and there currently is not a formal way of determining how many topics should be extracted using the LDA approach.
^{[4]} - While LDA is useful in the context of description alone, it can also be used in conjunction with supervised machine learning techniques and statistical algorithms in order to make predictions.
^{[4]} - To address this lacuna, we extend LDA by drawing topics from a Dirichlet process whose base distribution is a distribution over all strings rather than from a finite Dirichlet.
^{[5]} - N 1 %W PMLR %X Topic models based on latent Dirichlet allocation (LDA) assume a predefined vocabulary a priori.
^{[5]} - We show our model can successfully incorporate new words as it encounters new terms and that it performs better than online LDA in evaluations of topic quality and classification performance.
^{[5]} - In LDA, documents are represented as a mixture of topics and a topic is a bunch of words.
^{[6]} - LDA looks at a document to determine a set of topics that are likely to have generated that collection of words.
^{[6]} - LDA consists of two parts, the words within a document (a known factor) and the probability of words belonging to a topic, which is what needs to be calculated.
^{[6]} - The C code for LDA from David M. Blei and co-authors is used to estimate and fit a latent dirichlet allocation model with the VEM algorithm.
^{[7]} - We will look at what this means in practice in the LDA case with a simple example.
^{[8]} - Then I introduce how LDA model is constructed.
^{[9]} - Finally, I discuss two variations of conventional LDA including paralleled LDA and Online LDA.
^{[9]} - Three examples of using LDA outputs as building blocks for more complicated machine learning system are also demonstrated: 1) Cascaded LDA for taxonomy building.
^{[9]} - Latent Dirichlet Allocation (LDA) is often used in natural language processing to find texts that are similar.
^{[10]} - Add the Latent Dirichlet Allocation module to your pipeline.
^{[10]} - Because LDA creates a large feature matrix from the text, you'll typically analyze a single text column.
^{[10]} - These parameters are specific to the scikit-learn implementation of LDA.
^{[10]} - The document topic probabilities of an LDA model are the probabilities of observing each topic in each document used to fit the LDA model.
^{[11]} - DocumentTopicProbabilities is a D-by-K matrix where D is the number of documents used to fit the LDA model, and K is the number of topics.
^{[11]} - We extend Latent Dirichlet Allocation (LDA) by explicitly allowing for the encoding of side information in the distribution over words.
^{[12]} - Results indicate that our model substantially improves topic cohesion when compared to the standard LDA model.
^{[12]} - LDA 시각화를 위해서는 pyLDAvis의 설치가 필요합니다.
^{[13]} - Our work marks the first successful learning method to infer latent information in the environment of price war by the LDA modeling, and sets an example for related competitive applications to follow.
^{[14]} - The key assumptions behind LDA is that each given documents is a mix of multiple topics.
^{[15]} - Given a set of documents, one can use the LDA framework to learn not only the topic mixture (distribution) that represents each document.
^{[15]} - We’ll use the LDA function from the topicmodels library to implement gibbs sampling method on the same set of raw documents and print out the result for you to compare.
^{[15]} - We use LDA to abstract topics from source code and a new metric (topic failure density) is proposed by mapping failures to these topics.
^{[16]} - LDA is an unsupervised machine learning technique, which has been widely used in latent topic information recognition from documents or corpuses.
^{[16]} - We run LDA on three projects and get the topic distribution of the components.
^{[16]} - Since LDA is a probability model, mining different versions of the source code may also lead to different topics.
^{[16]} - These two hyperparameters are required by LDA.
^{[17]} - LDA allows for ‘fuzzy’ memberships.
^{[17]} - Of course, the main reason you’d use LDA is to uncover the themes lurking in your data.
^{[17]} - There are a few ways of implementing LDA.
^{[17]} - We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora.
^{[18]} - 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.
^{[18]} - The aim of LDA is to find topics a document belongs to, based on the words in it.
^{[19]} - LDA represents documents as a mixture of topics.
^{[19]} - The applications of LDA need not be restricted to Natural Language Processing.
^{[19]} - I recently implemented a paper where we use LDA( along with a Neural Networks) to extract the scene-specific context of an image.
^{[19]} - Before getting into the details of the Latent Dirichlet Allocation model, let’s look at the words that form the name of the technique.
^{[20]} - ‘Dirichlet’ indicates LDA’s assumption that the distribution of topics in a document and the distribution of words in topics are both Dirichlet distributions.
^{[20]} - LDA assumes that documents are composed of words that help determine the topics and maps documents to a list of topics by assigning each word in the document to different topics.
^{[20]} - It is important to note that LDA ignores the order of occurrence of words and the syntactic information.
^{[20]} - Latent Dirichlet allocation (LDA) is a particularly popular method for fitting a topic model.
^{[21]} - In this chapter, we’ll learn to work with LDA objects from the topicmodels package, particularly tidying such models so that they can be manipulated with ggplot2 and dplyr.
^{[21]} - Besides estimating each topic as a mixture of words, LDA also models each document as a mixture of topics.
^{[21]} - We can then use the LDA() function to create a four-topic model.
^{[21]} - Moreover, LDA has been shown effective in topic model based information retrieval.
^{[22]} - We present five case studies designed to determine the accuracy and scalability of the LDA-based technique, as well as its relationships to software system size and to source code stability.
^{[22]} - The results of the studies also indicate that the accuracy of the LDA-based technique is not affected by the size of the subject software system or by the stability of its source code base.
^{[22]} - We performed the latent Dirichlet allocation (LDA) as a topic model using a Python library.
^{[23]} - Latent Dirichlet allocation (LDA), a generative probabilistic model of a corpus, is a commonly used topic model.
^{[23]} - We used LDA in the Gensim library in Python for topic modeling.
^{[23]} - We set λ as 0.6 and ran the LDA with 3000 Gibbs sampling iterations.
^{[23]} - ADM-LDA: an aspect detection model based on topic modelling using the structure of review sentences.
^{[24]} - Chang J (2011) lda: collapsed Gibbs sampling methods for topic models.
^{[24]} - WT-LDA: user tagging augmented LDA for web service clustering.
^{[24]} - Latent dirichlet allocation based blog analysis for criminal intention detection system.
^{[24]} - In LDA, each document may be viewed as a mixture of various topics where each document is considered to have a set of topics that are assigned to it via LDA.
^{[25]} - This is identical to probabilistic latent semantic analysis (pLSA), except that in LDA the topic distribution is assumed to have a sparse Dirichlet prior.
^{[25]} - For example, an LDA model might have topics that can be classified as CAT_related and DOG_related.
^{[25]} - The resulting model is the most widely applied variant of LDA today.
^{[25]}

### 소스

- ↑
^{1.0}^{1.1}^{1.2}^{1.3}Latent Dirichlet Allocation (LDA) Algorithm - ↑
^{2.0}^{2.1}^{2.2}akashii99/Topic-Modelling-with-Latent-Dirichlet-Allocation: Implementation of Blei, Andrew Ng, Jordan paper of LDA - ↑
^{3.0}^{3.1}^{3.2}Topic Modeling with Latent Dirichlet Allocation - ↑
^{4.0}^{4.1}^{4.2}^{4.3}Latent Dirichlet Allocation in predicting clinical trial terminations - ↑
^{5.0}^{5.1}^{5.2}Online Latent Dirichlet Allocation with Infinite Vocabulary - ↑
^{6.0}^{6.1}^{6.2}Latent Dirichlet Allocation (LDA) - ↑ R Documentation
- ↑ Topic Modeling: Latent Dirichlet Allocation
- ↑
^{9.0}^{9.1}^{9.2}Application of Latent Dirichlet Allocation in Online Content Generation - ↑
^{10.0}^{10.1}^{10.2}^{10.3}Latent Dirichlet Allocation: Module reference - Azure Machine Learning - ↑
^{11.0}^{11.1}Latent Dirichlet allocation (LDA) model - ↑
^{12.0}^{12.1}Word Features for Latent Dirichlet Allocation - ↑ 2) 잠재 디리클레 할당(Latent Dirichlet Allocation, LDA)
- ↑ Latent Dirichlet Allocation for Internet Price War
- ↑
^{15.0}^{15.1}^{15.2}Latent Dirichlet Allocation Using Gibbs Sampling - ↑
^{16.0}^{16.1}^{16.2}^{16.3}Predicting Component Failures Using Latent Dirichlet Allocation - ↑
^{17.0}^{17.1}^{17.2}^{17.3}Your Guide to Latent Dirichlet Allocation - ↑
^{18.0}^{18.1}Latent dirichlet allocation - ↑
^{19.0}^{19.1}^{19.2}^{19.3}A Beginner’s Guide to Latent Dirichlet Allocation(LDA) - ↑
^{20.0}^{20.1}^{20.2}^{20.3}What is Latent Dirichlet Allocation (LDA) - ↑
^{21.0}^{21.1}^{21.2}^{21.3}Text Mining with R - ↑
^{22.0}^{22.1}^{22.2}Bug localization using latent Dirichlet allocation - ↑
^{23.0}^{23.1}^{23.2}^{23.3}JMIR - Topic Modeling of Social Networking Service Data on Occupational Accidents in Korea: Latent Dirichlet Allocation Analysis - ↑
^{24.0}^{24.1}^{24.2}^{24.3}Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey - ↑
^{25.0}^{25.1}^{25.2}^{25.3}Latent Dirichlet allocation

## 메타데이터

### 위키데이터

- ID : Q269236