scispace - formally typeset
Search or ask a question

From where does the measure of pointwise mutual information originates from ? 


Best insight from top research papers

The measure of pointwise mutual information (PMI) originates from the work of Barlow (1985) and has been extensively used in some research communities for flagging suspicious coincidences . PMI is a measure of association used in information theory and is based on the ratio of the joint probability of two events to the product of their individual probabilities . It is important to note that PMI is sensitive to the marginals, with increased scores for sparser events . The measure of PMI has been applied in various fields, including characterizing databases in terms of association strength between compound structural features .

Answers from top 5 papers

More filters
Papers (5)Insight
The paper does not explicitly mention the origin of the measure of pointwise mutual information.
The measure of pointwise mutual information originates solely from information-theoretic principles.
The paper does not mention the origin of the measure of pointwise mutual information.
The measure of pointwise mutual information (PMI) is discussed in the paper as a measure of association between two events A and B.
The paper does not mention the origin of the measure of pointwise mutual information.

Related Questions

A brief explanation of mutual induction?5 answersMutual induction is a phenomenon extensively utilized in various fields. In the context of wireless charging systems for mobile phones, mutual induction is employed through transmitting and receiver coils to transfer energy wirelessly. Moreover, in the realm of separation logic provers, mutual induction is leveraged as a technique for automatically proving entailments by using mathematical induction. This method involves utilizing goal entailments and other derived entailments as hypotheses to mutually prove each other, enhancing the success rate of proving the desired entailment. Additionally, a mutual induction device for a temperature-control type low-voltage breakerincorporates a mutual inductor and communication cable to connect with external devices, ensuring optimal output power and temperature control while improving measuring precision and reducing material costs.
What is mutual information feature selection?4 answersMutual information feature selection is a method used to identify important input features in a dataset. It aims to reduce the dimensionality of the input space while maintaining or improving classification performance. This approach is based on the concept of mutual information, which measures the statistical dependence between two variables. By calculating the mutual information between each feature and the response variable, the method prioritizes features based on their relevance to the prediction task. Several papers propose different algorithms and techniques for mutual information feature selection. These include approaches that handle redundancy among features, quantify the degree of feature redundancy, and dynamically adjust penalty factors to balance relevance and redundancy. Overall, mutual information feature selection algorithms have been shown to effectively improve classification accuracy in various domains.
How does adjusted mutual information compare to other metrics for evaluating recommendation systems?5 answersAdjusted mutual information is a metric used for evaluating the similarity between two clusterings. It introduces a corrective term based on random permutations of labels to prevent chance similarity. However, this adjustment makes the metric computationally expensive. In a paper by Lazarenko and Bonald, they propose a novel adjustment based on pairwise label permutations instead of full label permutations. This adjusted metric has a much lower time complexity while still assessing clustering quality effectively. In another paper by Valcarce, Bellogín, Parapar, and Castells, they analyze the robustness and discriminative power of different ranking metrics for evaluating recommender systems. They find that precision provides high robustness, while normalized discounted cumulative gain offers the best discriminative power. Therefore, adjusted mutual information is a computationally efficient metric for clustering evaluation, while precision and normalized discounted cumulative gain are effective metrics for recommender system evaluation.
What are some related studies that use point biserial correlation?5 answersThe point-biserial correlation has been used in several related studies. Bonett proposed new estimators of point-biserial correlation that can accommodate unequal variances and derived confidence intervals and standard errors for these estimators. Jacobs and Viechtbauer discussed the estimation of the biserial correlation coefficient for meta-analytic purposes and compared different methods for estimating its sampling variance. Chueh proposed a fuzzy point-biserial correlation analysis method to investigate linear relationships between crisp and fuzzy attributes. Kornbrot described the point biserial correlation as a measure of effect size in terms of the proportion of variability accounted for by the relation between a dichotomous and a metric variable.
Has the concept of entropy been used to quantify correlations in correlation matrices?5 answersEntropy measures have been used to quantify correlations in correlation matrices. The concept of entropy has been extended to various fields, including physics, information theory, and economics. In the context of quantum systems, entropy measures such as von Neumann and R\'enyi entropies have been estimated using variational quantum algorithms. In molecular systems, a kinematic measure of entanglement called "coupled entropy" has been proposed, which accurately describes the Einstein-Podolsky-Rosen entanglement. Quantum coarse-grained entropy, known as "quantum correlation entropy," has been studied as a generalization of entanglement entropy to mixed states and multipartite systems. Additionally, a quantitative connection has been established between correlation functions and entanglement, where the zeros of local correlation matrices provide an upper bound on entanglement entropy. These studies demonstrate the use of entropy to quantify correlations in correlation matrices in different contexts.
What are the common measures of information sharing?3 answersCommon measures of information sharing include integration readiness level (IRL) metrics, shared information systems, shared information associated with application programs, and shared information about posted messages. IRL metrics are used to assess the readiness level of shared information services and systems, improving acceptance, validation, risk assessment, and development of sharing mechanisms. Information sharing systems involve executing units, recording units, generating units, and sharing units to facilitate the sharing of information among users. Shared information associated with application programs is generated based on user operations and shared with multiple users. In the context of home devices, an information common share device manages and informs users about the idle state of reserved devices and message contents. In the context of posted messages, an information sharing system allows users to share question and answer messages, with restrictions based on positional information.