# Tag Info

Accepted

### Latent Class Analysis vs. Cluster Analysis - differences in inferences?

Latent Class Analysis is in fact an Finite Mixture Model (see here). The main difference between FMM and other clustering algorithms is that FMM's offer you a "model-based clustering" approach that ...
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### What is principal subspace in probabilistic PCA?

This is an excellent question. Probabilistic PCA (PPCA) is the following latent variable model \begin{align} \mathbf z &\sim \mathcal N(\mathbf 0, \mathbf I) \\ \mathbf x &\sim \mathcal N(\...
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### LDA vs word2vec

An answer to Topic models and word co-occurrence methods covers the difference (skip-gram word2vec is compression of pointwise mutual information (PMI)). So: neither method is a generalization of ...
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### How to choose an optimal number of latent factors in non-negative matrix factorization?

To choose an optimal number of latent factors in non-negative matrix factorization, use cross-validation. As you wrote, the aim of NMF is to find low-dimensional $\mathbf W$ and $\mathbf H$ with all ...

### LDA vs word2vec

The two algorithms differ quite a bit in their purpose. LDA is aimed mostly at describing documents and document collections by assigning topic distributions to them, which in turn have word ...
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### When a CFA model has a "covariance matrix was not positive definite" problem, is it due to the dataset or the model?

The covariance matrix of the data is always non-negative definite, there is no doubt about that. However, the model-implied covariance matrix may not be when some parameters take values outside their ...
Expectations are central to the EM algorithm. To start with, the likelihood associated with the data $(x_1,\ldots,x_n)$ is represented as an expectation \begin{align*} p(x_1,\ldots,x_n;\theta) &= \...