Which additional technique serves to complement LDA in discovering latent topics within text?

Non-Negative Matrix Factorization (NMF) is an additional technique that complements Latent Dirichlet Allocation (LDA) in the discovery of latent topics within text. Both LDA and NMF are used for topic modeling, but they approach the task differently, which allows them to provide unique insights into the structure of the data.

NMF works by factorizing the document-term matrix into two lower-dimensional matrices, effectively identifying the latent topics as combinations of words. This method adheres to the property that all components are non-negative, making it particularly useful in contexts like text data, where negative values do not have a meaningful interpretation. Consequently, NMF can yield a clearer and interpretable representation of underlying topics that can complement the probabilistic modeling approach used by LDA.

While other options have their own utility in data analysis and machine learning, they do not directly align with the goal of uncovering latent topics in the same way that NMF does alongside LDA. Hierarchical Clustering, for instance, is more about grouping data points based on similarity rather than topic extraction. Principal Component Analysis (PCA) aims at reducing dimensionality and finding variance in data rather than directly identifying latent topics. Support Vector Machines (SVM) are