Unsupervised Learning by Probabilistic Latent Semantic Analysis

By Thomas Hofmann. 2004

2,727 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing, Probabilistic & Bayesian Methods, Signal Processing

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Citations in the corpus

Using Multiple Segmentations to Discover Objects and their Extent in Image Collections (2006, methodology): “These tools, such as probabilistic Latent Semantic Analysis (pLSA) [12] and Latent Dirichlet Allocation (LDA) [2], use unordered “bag of words” representation of documents to automatically discover topics in a large text corpus.”
Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning (2015, positive): “In the first step, the CNN features [29] are clustered using probabilistic latent semantic analysis (PLSA) [25].”
Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, background): “A typical example of unsupervised learning is clustering data into groups with uncountable applications and one good example for an unsupervised method is probabilistic latent semantic analysis (PLSA) [20], which is very helpful in text mining [21].”
Composition in Distributional Models of Semantics (2010, methodology): “Dimensionality reduction techniques can be also used to project high-dimensional vectors onto a lower dimensional space (Blei et al., 2003; Hofmann, 2001; Landauer & Dumais, 1997).”
Software traceability with topic modeling (2010, background): “For this data set, we see that LDA performs better than LSI, perhaps due to the fact that LDA is fully probabilistic and includes priors on . and f which can act as regularization.”
Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification (2017, methodology): “The traditional HSR remote sensing imagery scene classification methods include bag of visual words (BOVW) [9–12], spatial pyramid matching (SPM) [13], latent Dirichlet allocation (LDA) [14–16], and probabilistic latent semantic allocation (PLSA) [17,18].”
Cross-Lingual Latent Topic Extraction (2010, methodology): “To calculate the likelihood of ﬁtness, we use the folding-in method proposed in (Hofmann, 2001).”
Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article] (2014, background): “Some examples of this are: (a) latent semantic analysis (Hofmann, 2001), where documents are represented as vectors in a term space; (b) latent Dirichlet allocation (Porteous et al., 2008), which involves attributing document terms to topics; (c) MapReduce (C. Liu, Qi, Wang, & Yu, 2012), a…”
Robustness of collaborative recommendation based on association rule mining (2007, methodology): “In the maximization step, Lagrange multipliers [5] are used to obtain the following equations for re-estimated parameters:”
An Empirical Study of Remote Sensing Pretraining (2022, methodology): “The typical unsupervised learning methods include potential latent semantic analysis [9] and bag of visual words [10].”
Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation (2010, background): “Generative probabilistic models such as probabilistic latent semantic analysis (pLSA) [10] and latent Dirichlet allocation (LDA) [11] have been exploited for this purpose.”
Comment-based multi-view clustering of web 2.0 items (2014, methodology): “Bruno et al. [7] treat the optimal clustering as hidden factors to generate the clustering of the different views, and then adopt PLSA [18] to solve the problem.”
Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity (2013, neutral): “Later efforts such as LSI (Deer-wester et al., 1990), PLSA (Hofmann, 2001) and Topic Models (Blei et al., 2003; Steyvers and Grif-ﬁths, 2007) overcame these sparsity issues using dimensionality reduction techniques or modeling the document using latent variables.”
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning (2018, methodology): “A common approach for dimensionality reduction makes use of the Singular Value Decomposition (SVD), also known as Latent Semantic Analysis (Hofmann, 2001; Landauer & Dooley, 2002, LSA).”
Collaborative filtering via gaussian probabilistic latent semantic analysis (2003, methodology): “While we have investigated a modification of EM, called tempered EM in [10], a conceptually simpler and computationally less expensive approach has proven to be sufficient for regularizing Gaussian pLSA models.”