Sparse topic modeling via spectral decomposition and thresholding

TL;DR

This paper introduces a new spectral thresholding method for sparse topic modeling that effectively estimates the topic-word matrix even in high-dimensional settings and with non-separable data, outperforming existing methods.

Contribution

The paper proposes a novel spectral procedure incorporating thresholding based on word frequencies, addressing high-dimensional and non-separable data scenarios in topic modeling.

Findings

Error rate depends logarithmically on vocabulary size

Method is computationally fast and consistent across various regimes

Performs well on research papers, single-cell, and microbiome data

Abstract

The probabilistic Latent Semantic Indexing model assumes that the expectation of the corpus matrix is low-rank and can be written as the product of a topic-word matrix and a word-document matrix. In this paper, we study the estimation of the topic-word matrix under the additional assumption that the ordered entries of its columns rapidly decay to zero. This sparsity assumption is motivated by the empirical observation that the word frequencies in a text often adhere to Zipf's law. We introduce a new spectral procedure for estimating the topic-word matrix that thresholds words based on their corpus frequencies, and show that its $\ell_1$-error rate under our sparsity assumption depends on the vocabulary size $p$ only via a logarithmic term. Our error bound is valid for all parameter regimes and in particular for the setting where $p$ is extremely large; this high-dimensional setting is commonly encountered but has not been adequately addressed in prior literature. Furthermore, our procedure also accommodates datasets that violate the separability assumption, which is necessary for most prior approaches in topic modeling. Experiments with synthetic data confirm that our procedure is computationally fast and allows for consistent estimation of the topic-word matrix in a wide variety of parameter regimes. Our procedure also performs well relative to well-established methods when applied to a large corpus of research paper abstracts, as well as the analysis of single-cell and microbiome data where the same statistical model is relevant but the parameter regimes are vastly different.