jsPCA

matrix, with each row containing the distribution over terms
for a topic, with as many rows as there are topics in the model, and as
many columns as there are terms in the vocabulary.


Dimension reduction via Jensen-Shannon Divergence & Principal Components


Tools to create an interactive web-based visualization of a
topic model that has been fit to a corpus of text data using
Latent Dirichlet Allocation (LDA). Given the estimated parameters of
the topic model, it computes various summary statistics as input to
an interactive visualization built with D3.js that is accessed via
a browser. The goal is to help users interpret the topics in their
LDA topic model.

jsPCA: Dimension reduction via Jensen-Shannon Divergence & Principal Components

Description

Usage

Arguments