A New Spectral Method for Latent Variable Models
This work addresses the problem of efficiently learning parameters for text mining models, but it appears incremental as it builds on existing spectral methods for known models.
The paper tackles unsupervised learning of latent variable models using a spectral decomposition-based algorithm, demonstrating robustness in theory and practice with applications to single topic models and Latent Dirichlet Allocation on synthetic and real-world text corpora.
This paper presents an algorithm for the unsupervised learning of latent variable models from unlabeled sets of data. We base our technique on spectral decomposition, providing a technique that proves to be robust both in theory and in practice. We also describe how to use this algorithm to learn the parameters of two well known text mining models: single topic model and Latent Dirichlet Allocation, providing in both cases an efficient technique to retrieve the parameters to feed the algorithm. We compare the results of our algorithm with those of existing algorithms on synthetic data, and we provide examples of applications to real world text corpora for both single topic model and LDA, obtaining meaningful results.