# Probabilistic Latent Semantic Analysis

arXiv: https://arxiv.org/abs/1301.6705 · HTML: https://mlsanity.com/paper/1301.6705/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2013.

## Built on by (most cited first, 30 of 63)

- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Effective Neural Topic Modeling with Embedding Clustering Regularization](https://mlsanity.com/paper/2306.04217/) (2023)
- [Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning](https://mlsanity.com/paper/2211.12878/) (2022)
- [FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model](https://mlsanity.com/paper/2405.17978/) (2024)
- [Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks](https://mlsanity.com/paper/2104.00853/) (2021)
- [Prompting Large Language Models for Topic Modeling](https://mlsanity.com/paper/2312.09693/) (2023)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization](https://mlsanity.com/paper/2205.11025/) (2022)
- [Persona-Based Conversational AI: State of the Art and Challenges](https://mlsanity.com/paper/2212.03699/) (2022)
- [Few-shot Learning for Topic Modeling](https://mlsanity.com/paper/2104.09011/) (2021)
- [Learning Mathematical Properties of Integers](https://mlsanity.com/paper/2109.07230/) (2021)
- [GINopic: Topic Modeling with Graph Isomorphism Network](https://mlsanity.com/paper/2404.02115/) (2024)
- [Provable unlearning in topic modeling and downstream tasks](https://mlsanity.com/paper/2411.12600/) (2024)
- [Comparative Analysis of Topic Modeling Techniques on ATSB Text Narratives Using Natural Language Processing](https://mlsanity.com/paper/2501.01227/) (2025)
- [A Distributional Perspective on Word Learning in Neural Language Models](https://mlsanity.com/paper/2502.05892/) (2025)
- [Efficient Algorithms for Sparse Moment Problems without Separation](https://mlsanity.com/paper/2207.13008/) (2022)
- [Topic Modeling on Clinical Social Work Notes for Exploring Social Determinants of Health Factors](https://mlsanity.com/paper/2212.01462/) (2022)
- [Determination of the Number of Topics Intrinsically: Is It Possible?](https://mlsanity.com/paper/2406.10402/) (2024)
- [BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study](https://mlsanity.com/paper/2501.03843/) (2025)
- [Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning](https://mlsanity.com/paper/2109.13233/) (2021)
- [Learning Topic Models: Identifiability and Finite-Sample Analysis](https://mlsanity.com/paper/2110.04232/) (2021)
- [Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index](https://mlsanity.com/paper/2111.00057/) (2021)
- [TopNet: Learning from Neural Topic Model to Generate Long Stories](https://mlsanity.com/paper/2112.07259/) (2021)
- [Embeddings for Tabular Data: A Survey](https://mlsanity.com/paper/2302.11777/) (2023)
- [Persona-Coded Poly-Encoder: Persona-Guided Multi-Stream Conversational Sentence Scoring](https://mlsanity.com/paper/2309.16770/) (2023)
- [Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of Topic Models](https://mlsanity.com/paper/2502.14748/) (2025)
- [Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendation](https://mlsanity.com/paper/2508.11105/) (2025)
- [AI supported Topic Modeling using KNIME-Workflows](https://mlsanity.com/paper/2104.09428/) (2021)
- [Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts](https://mlsanity.com/paper/2109.12306/) (2021)
- [Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization](https://mlsanity.com/paper/2201.00687/) (2021)

## Abstract (arXiv)

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.