Probabilistic Latent Semantic Analysis (pLSA) is a powerful technique for discovering hidden topics in large text collections, enabling efficient document classification and information retrieval. pLSA is a statistical method that uncovers latent topics within a collection of documents by analyzing the co-occurrence of words. It uses a probabilistic approach to model the relationships between words and topics, as well as between topics and documents. By identifying these hidden topics, pLSA can help in tasks such as document classification, information retrieval, and content analysis. Recent research in pLSA has focused on various aspects of the technique, including its formalization, learning algorithms, and applications. For instance, one study explored the use of pLSA for classifying Indonesian text documents, while another investigated its application in modeling loosely annotated images. Other research has sought to improve pLSA's performance by incorporating word embeddings, neural networks, and other advanced techniques. Some notable arxiv papers on pLSA include: 1. A tutorial on Probabilistic Latent Semantic Analysis by Liangjie Hong, which provides a comprehensive introduction to the formalization and learning algorithms of pLSA. 2. Probabilistic Latent Semantic Analysis (PLSA) untuk Klasifikasi Dokumen Teks Berbahasa Indonesia by Derwin Suhartono, which discusses the application of pLSA in classifying Indonesian text documents. 3. Discovering topics with neural topic models built from PLSA assumptions by Sileye 0. Ba, which presents a neural network-based model for unsupervised topic discovery in text corpora, leveraging pLSA assumptions. Practical applications of pLSA include: 1. Document classification: pLSA can be used to automatically categorize documents based on their content, making it easier to manage and retrieve relevant information. 2. Information retrieval: By representing documents as a mixture of latent topics, pLSA can improve search results by considering the semantic relationships between words and topics. 3. Content analysis: pLSA can help analyze large text collections to identify trends, patterns, and themes, providing valuable insights for decision-making and strategy development. A company case study that demonstrates the use of pLSA is Familia, a configurable topic modeling framework for industrial text engineering. Familia supports a variety of topic models, including pLSA, and enables software engineers to easily explore and customize topic models for their specific needs. By providing a scalable and efficient solution for topic modeling, Familia has been successfully applied in real-life industrial applications. In conclusion, pLSA is a powerful technique for discovering hidden topics in large text collections, with applications in document classification, information retrieval, and content analysis. Recent research has sought to improve its performance and applicability by incorporating advanced techniques such as word embeddings and neural networks. By connecting pLSA to broader theories and frameworks, researchers and practitioners can continue to unlock its potential for a wide range of text engineering tasks.
Pairwise Ranking
What is the pairwise ranking?
Pairwise ranking is a machine learning technique used to rank items by comparing them in pairs and determining their relative order based on these comparisons. It is widely applied in various fields, such as citation analysis, protein domain ranking, and medical image quality assessment. Researchers have developed different algorithms and models to improve the accuracy and efficiency of pairwise ranking, addressing challenges like reducing annotation burden, handling missing or corrupted comparisons, and accounting for biases in crowdsourced pairwise comparisons.
What is an example of pairwise ranking?
An example of pairwise ranking is ranking academic journals based on their influence in a specific field. By comparing journals in pairs and determining their relative order, researchers can create a ranking that reflects the journals" impact and relevance in the field. This can help researchers identify the most influential journals to publish their work or to find relevant research articles.
What is a pairwise comparison?
A pairwise comparison is a process of comparing two items or objects to determine their relative order or preference. In the context of pairwise ranking, pairwise comparisons are used to gather information about the relative order of items in a list, which can then be used to create a complete ranking of the items.
Why do we use pairwise comparison?
Pairwise comparison is used because it simplifies complex ranking problems by breaking them down into smaller, more manageable comparisons. By comparing items in pairs, it is easier to determine their relative order and create a complete ranking. Pairwise comparison also allows for the incorporation of various algorithms and models to improve the accuracy and efficiency of the ranking process.
What is paired comparison and ranking?
Paired comparison and ranking is a method that involves comparing items in pairs to determine their relative order or preference. This method is used in various fields, such as machine learning, psychology, and decision-making, to rank items based on their importance, relevance, or quality. Paired comparison and ranking can be applied to various types of data, including ordinal, interval, and ratio data.
What is pairwise in hockey?
Pairwise in hockey refers to a ranking system used by the NCAA to determine the seeding of teams in the national tournament. The pairwise ranking system compares teams in pairs based on various criteria, such as head-to-head results, strength of schedule, and overall win-loss record. This pairwise comparison method helps create a fair and accurate ranking of teams for the tournament.
How does pairwise ranking work in machine learning?
In machine learning, pairwise ranking works by comparing items in pairs and using the results of these comparisons to create a complete ranking. Various algorithms and models can be applied to improve the accuracy and efficiency of the ranking process. Some common approaches include incorporating empirical Bayes methods, spectral seriation, and graph regularization. These methods help address challenges like reducing annotation burden, handling missing or corrupted comparisons, and accounting for biases in crowdsourced pairwise comparisons.
What are some practical applications of pairwise ranking?
Practical applications of pairwise ranking include ranking academic journals based on their influence in a specific field, identifying the most relevant protein domains in structural biology, and assessing the quality of medical images for diagnostic purposes. Pairwise ranking can also be applied in other domains, such as sports rankings, product recommendations, and search engine optimization.
What are some notable research papers on pairwise ranking?
Some notable research papers on pairwise ranking include: 1. 'Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis' by Jiaying Gu and Roger Koenker, which adapts the pairwise comparison model for ranking and selection of journal influence. 2. 'Spectral Ranking using Seriation' by Fajwel Fogel, Alexandre d"Aspremont, and Milan Vojnovic, which introduces a seriation algorithm for ranking items based on pairwise comparisons and demonstrates its robustness to noise. 3. 'Active Ranking using Pairwise Comparisons' by Kevin G. Jamieson and Robert D. Nowak, which proposes an adaptive algorithm for ranking objects using pairwise comparisons under the assumption that objects can be embedded in a Euclidean space.
How can pairwise ranking be used in search engine optimization (SEO)?
Pairwise ranking can be used in search engine optimization (SEO) to improve the ranking of web pages in search engine results. By comparing web pages in pairs based on various factors, such as relevance, quality, and user engagement, pairwise ranking algorithms can create a more accurate and efficient ranking of web pages. This can help search engines provide better search results to users and improve the overall user experience.
Pairwise Ranking Further Reading
1.Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis http://arxiv.org/abs/2112.11064v1 Jiaying Gu, Roger Koenker2.Spectral Ranking using Seriation http://arxiv.org/abs/1406.5370v4 Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic3.Simultaneous Rank Tests in Analysis of Covariance Based on Pairwise Ranking http://arxiv.org/abs/1802.03884v1 Hossein Mansouri, Fangyuan Zhang4.Active Ranking using Pairwise Comparisons http://arxiv.org/abs/1109.3701v2 Kevin G. Jamieson, Robert D. Nowak5.Aggregation of pairwise comparisons with reduction of biases http://arxiv.org/abs/1906.03711v1 Nadezhda Bugakova, Valentina Fedorova, Gleb Gusev, Alexey Drutsa6.Pairwise Ranking with Gaussian Kernels http://arxiv.org/abs/2304.03185v1 Guanhang Lei, Lei Shi7.Multiple graph regularized protein domain ranking http://arxiv.org/abs/1208.3779v3 Jim Jing-Yan Wang, Halima Bensmail, Xin Gao8.Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating http://arxiv.org/abs/2202.04823v1 Ikbeom Jang, Garrison Danley, Ken Chang, Jayashree Kalpathy-Cramer9.PromptShots at the FinNLP-2022 ERAI Tasks: Pairwise Comparison and Unsupervised Ranking http://arxiv.org/abs/2301.06606v1 Peratham Wiriyathammabhum10.Density-Ratio Based Personalised Ranking from Implicit Feedback http://arxiv.org/abs/2101.07481v1 Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi SatohExplore More Machine Learning Terms & Concepts
PLSA (Probabilistic Latent Semantic Analysis) Panoptic Segmentation Panoptic segmentation is a computer vision task that unifies instance segmentation and semantic segmentation, providing a comprehensive understanding of a scene by identifying and classifying every pixel. Panoptic segmentation has gained significant attention in recent years, with researchers developing various methods to tackle this challenge. One approach involves ensembling instance and semantic segmentation separately and then combining the results to generate panoptic segmentation. Another method focuses on video panoptic segmentation, which extends the task to video sequences and requires tracking instances across frames. This has led to the development of end-to-end trainable algorithms using transformers for video panoptic segmentation. Recent research has also explored the integration of panoptic segmentation with other tasks, such as visual odometry and LiDAR point cloud segmentation. For example, the Panoptic Visual Odometry (PVO) framework combines visual odometry and video panoptic segmentation to improve scene modeling and motion estimation. Similarly, Panoptic-PolarNet is a proposal-free LiDAR point cloud panoptic segmentation framework that leverages a polar Bird's Eye View representation to address occlusion issues in urban street scenes. Uncertainty-aware panoptic segmentation is another emerging area, aiming to predict per-pixel semantic and instance segmentations along with per-pixel uncertainty estimates. This approach can enhance the reliability of scene understanding for autonomous systems operating in real-world environments. Practical applications of panoptic segmentation include assisting visually impaired individuals in navigation by providing a holistic understanding of their surroundings, improving the perception stack for autonomous vehicles, and enhancing domain adaptation for panoptic segmentation in synthetic-to-real contexts. One company case study involves the development of the Efficient Panoptic Segmentation (EfficientPS) architecture, which sets a new state-of-the-art performance on multiple benchmarks while being highly efficient and fast. This architecture can be applied to autonomous robots, enabling them to better understand and navigate complex environments. In conclusion, panoptic segmentation is a rapidly evolving field with numerous applications and research directions. By unifying instance and semantic segmentation, it offers a more comprehensive understanding of scenes, which can be leveraged in various industries, including robotics, autonomous vehicles, and assistive technologies for the visually impaired.
