Does LDA need labels?
Your understanding is correct: you need to label each input document before training. Labelled LDA is a supervised method, meaning that you need a labelled dataset. If you “have to use Labelled LDA” you cannot get away from the need to obtained a labelled dataset.
Can SVM be used for multi label classification?
In its most basic type, SVM doesn’t support multiclass classification. For multiclass classification, the same principle is utilized after breaking down the multi-classification problem into smaller subproblems, all of which are binary classification problems.
What is labeled LDA?
Labeled LDA is a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA’s latent topics and user tags. Labeled LDA can directly learn topics(tags) correspondences.
Is Latent Dirichlet Allocation supervised?
That’s right that LDA is an unsupervised method. However, it could be extended to a supervised one.
When to use LDA for non linear separability?
However, if the dataset is not linear separable the LDA will try to organize your dataset in another space as the maximum linearly separability as possible, but it still be examples overlapping between classes because of non-linearly characteristic of data.
How to train a classifier for LDA model?
Since we have found a good number of topics for our LDA model, which is roughly around 15 topics with minor deviations between different runs, we can now train a classifier. The classifier of our choice is SVM with a linear kernel.
How is LDA used for dimensionality reduction in data science?
Firstly, PCA acts reducing the features by its variance, then LDA apply linear dimensionality reduction and lastly a classifier model is performed over this modified dataset. It is definitely a roadmap to be followed in order to improve the results in a classification tasks.
When to use LDA in linear discriminant analysis?
The main of Linear Discriminant Analysis is basically separate example of classes linearly moving them to a different feature space, therefore if your dataset is linear separable, only applying LDA as a classifier you will get great results.