## What is Plsa in machine learning?

pLSA stands for Probabilistic Latent Semantic Analysis, uses a probabilistic method instead of Singular Value Decomposition, which we used in LSA to tackle the problem. The main goal is to find a probabilistic model with latent or hidden topics that can generate the data which we observe in our document-term matrix.

### Is latent semantic analysis supervised or unsupervised?

unsupervised

If we talk about whether it is supervised or unsupervised way, it is clearly an unsupervised approach. It is a very helpful technique in the reduction of dimensions of the matrix or topic modeling and is also known as Latent Semantic Indexing(LSI).

#### What is Latent Semantic Analysis example?

We’ll implement LSA using a small example that will help us understand the working and output of LSA. a1 = “He is a good dog.” a2 = “The dog is too lazy.” a3 = “That is a brown cat.”

**How is LDA different from LSA?**

Unlike LSA, LDA does not directly output document similarities. Instead, LDA outputs a matrix, z, whose rows represent all the words in the dataset, and columns represent all the documents. Each value in the matrix represents a topic that the word represented by the row and column is assigned to by the LDA algorithm.

**What is EM algorithm used for?**

Introduction. The EM algorithm is used to find (local) maximum likelihood parameters of a statistical model in cases where the equations cannot be solved directly. Typically these models involve latent variables in addition to unknown parameters and known data observations.

## What is difference between LDA and LSA?

Both LSA and LDA have same input which is Bag of words in matrix format. LSA focus on reducing matrix dimension while LDA solves topic modeling problems.

### What is the relationship between LSA and PCA?

1) Essentially LSA is PCA applied to text data. The difference is PCA often requires feature-wise normalization for the data while LSA doesn’t.

#### Does LDA use TF IDF?

Choosing the top V words by TFIDF is an effective way to prune the vocabulary”. This said, LDA does not need tf-idf to infer topics, but it can be useful and it can improve your results.

**Which is better LSA or LDA?**

Both LSA and LDA have same input which is Bag of words in matrix format. LSA focus on reducing matrix dimension while LDA solves topic modeling problems. I will not go through mathematical detail and as there is lot of great material for that. You may check it from reference.

**What is the difference between K mean and EM?**

EM and K-means are similar in the sense that they allow model refining of an iterative process to find the best congestion. However, the K-means algorithm differs in the method used for calculating the Euclidean distance while calculating the distance between each of two data items; and EM uses statistical methods.

## Is EM algorithm supervised or unsupervised?

The Expectation Maximization (EM) algorithm is one approach to unsuper- vised, semi-supervised, or lightly supervised learning.

### Is LSA a topic model?

Latent Semantic Analysis, or LSA, is one of the foundational techniques in topic modeling. The core idea is to take a matrix of what we have — documents and terms — and decompose it into a separate document-topic matrix and a topic-term matrix.

#### Why is LSA better than LDA?

**Where is Latent Semantic Analysis used?**

The LSA is used in search engines. Latent Semantic Indexing(LSI) is the algorithm developed on LSA. The documents matching the search query are found using the vector developed from LSA. LSA can also be used for document clustering.

**Is LDA supervised or unsupervised?**

LDA is unsupervised by nature, hence it does not need predefined dictionaries. This means it finds topics automatically, but you cannot control the kind of topics it finds. That’s right that LDA is an unsupervised method. However, it could be extended to a supervised one.

## Which is the best topic Modelling algorithm?

The best and frequently used algorithm to define and work out with Topic Modeling is LDA or Latent Dirichlet Allocation that digs out topic probabilities from statistical data available.