Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and Insights
Abstract
:1. Introduction
2. The Method: PLSA Formulas
Aspect 1 | Aspect 2 | Aspect 3 | Aspect 4 |
imag | video | region | speaker |
SEGMENT | sequenc | contour | speech |
color | motion | boundari | recogni |
tissu | frame | descript | signal |
Aspect1 | scene | imag | train |
brain | SEGMENT | SEGMENT | hmm |
slice | shot | precis | sourc |
cluster | imag | estim | speakerindepend |
mri | cluster | pixel | SEGMENT |
algorithm | visual | paramet | sound |
3. Criticism: LDA and Reformulations
3.1. Latent Dirichlet Allocation
3.2. Other Formulations
3.2.1. Probabilities for Unseen Documents
3.2.2. Extension to Continuous Data
3.2.3. Tensorial Approach
3.2.4. Overfitting
3.2.5. Discrete and Continuous Variables Case Equivalence
3.2.6. Inference
3.3. Extensions Significance
4. The Landscape of Applications
4.1. Engineering
4.2. Computer Science
4.3. Semantic Image Analysis
4.4. Life Sciences
4.5. Fundamental Sciences
4.6. Other Applications
5. NMF Point of View
6. Extensions
6.1. Kernelization
6.2. Principal Component Analysis
6.3. Clustering
6.4. Information Theory Interpretation
6.5. Independent Component Analysis and Blind Source Separation
6.6. Transfer Learning
6.7. Neuronal Networks
6.8. Open Questions
7. PLSA Processing Steps and State-of-the-Art Solutions
7.1. Algorithm Initialization
7.2. Algorithms Based on Expectation–Maximization Improvement
7.2.1. Tempered EM
7.2.2. Sparse PLSA
7.2.3. Incremental PLSA
7.3. Use of Computational Techniques
7.4. Open Questions
8. Future Work
9. Discussion
10. Conclusions
Funding
Conflicts of Interest
References
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Year | Contribution | Remarks |
---|---|---|
2000 | PLSA | PLSA formulation in conference proceedings [1,2,3] comments on the connections among NMF, SVD, and information geometry. |
2001 | Kernelization | Fisher kernel derivation from PLSA [17]. |
2003 | LDA | Criticism of PLSA: LDA formulation [23]. |
2003 | Gaussian PLSA | Assumption of Gaussian mixtures [11]. |
2005 | NMF | PLSA solves the NMF problem [14]. Introduction to stochastic matrices [15]. |
2008 | k-means | Equivalence between k-means and NMF [24]. |
2009 | PCA | Comparison of NMF, PLSA, and PCA [19]. |
2012 | Information Geometry | Relationship between Fisher information matrix and variance from the PLSA context [20]. |
2013 | Transfer Learning | Use of latent variables weight for classifying most relevant variables [21]. |
2015 | Unified framework for PLSA and NMF. | Algorithm for NMF and PLSI based on Poisson likelihood [25]. |
2019 | Neural Networks | Neural networks training with PLSA [22]. |
2020 | SVD | Establishment of conditions for equivalence of NMF, PLSA, and SVD [16]. |
2020 | Inference | Construction of hypothesis tests [13] |
2021 | Number of topics | NMF and Silhouette index to determine the number of latent variables [26]. |
2023 | Discrete and continuous case equivalence. | Relation between co-occurrences and continuous variables [12]. |
Asymmetric Formulation | Symmetric Formulation | |
---|---|---|
E-step | ||
M-step | ||
Discipline | Research Area | % |
---|---|---|
Engineering (43%) | Mechanics & Robotics | 35 |
Acoustics | 4 | |
Telecommunications & Control Theory | 3 | |
Materials Science | 1 | |
Computer Science (34%) | Clustering | 18 |
Information retrieval | 9 | |
Networks | 4 | |
Machine learning applications | 3 | |
Semantic image analysis (10%) | Image annotation | 4 |
Image retrieval | 3 | |
Image classification | 3 | |
Life Sciences (5%) | Computational Biology | 2 |
Biochemistry & Molecular Biology | 2 | |
Environmental Sciences Ecology | 1 | |
Methodological (4%) | Statistics & Computational Techniques | 4 |
Fundamental Sciences (2%) | Geochemistry & Geophysics | 1 |
Instrumentation | 1 | |
Other Applications (2%) | Pain Detection | 1 |
Sentiment Analysis | 1 |
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Figuera, P.; García Bringas, P. Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and Insights. Technologies 2024, 12, 5. https://doi.org/10.3390/technologies12010005
Figuera P, García Bringas P. Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and Insights. Technologies. 2024; 12(1):5. https://doi.org/10.3390/technologies12010005
Chicago/Turabian StyleFiguera, Pau, and Pablo García Bringas. 2024. "Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and Insights" Technologies 12, no. 1: 5. https://doi.org/10.3390/technologies12010005