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Everything about Random indexing
Dimensionality reduction method for distributional semantics
Random indexing is a dimensionality reduction method and computational framework for distributional semantics, based on the insight that very-high-dimensional vector space model implementations are impractical, that models need not grow in dimensionality when new items are encountered, and that a high-dimensional model can be projected into a space of lower dimensionality without compromising L2 distance metrics if the resulting dimensions are chosen appropriately. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Random indexingA 5-minute orientation
- Skim a recent paper: Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration AnalysisInfluential work since 2022
- Pick up a classic book: Index on CensorshipThe most-reprinted book on the subject
- Read the landmark paper: Failure prediction based on log files using Random Indexing and Support Vector MachinesCited 93 times
- Go sideways: Pentti KanervaA closely related topic
- Go sideways: Distributional semanticsA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
No outline available.
🏆 Landmark papers (most cited)
Failure prediction based on log files using Random Indexing and Support Vector Machines
Reflective Random Indexing and indirect inference: A scalable method for discovery of implicit connections
Automatic bilingual lexicon acquisition using random indexing of parallel corpora
Web user clustering and Web prefetching using Random Indexing with weight functions
Reflective random indexing for semi-automatic indexing of the biomedical literature
Random Indexing and Modified Random Indexing based approach for extractive text summarization
Novel chaotic random memory indexing steganography on FPGA
A random walk on an ontology: Using thesaurus structure for automatic subject indexing
🆕 Influential since 2022
📚 Books worth reading
- 📕Index on CensorshipWriters and Scholars International Staff · 1977
The Drunkard's WalkLeonard Mlodinow · 2008
The limerick; 1700 examplesG. Legman · 1969
The G.I. dietRick Gallop · 2002
The Fear IndexRobert Harris · 2011
Probability, random variables, and random signal principlesPeyton Z. Peebles · 1980
Limit Theorems for Multi-Indexed Sums of Random VariablesOleg Klesov · 2014
Running Randomized EvaluationsRachel Glennerster · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Pentti Kanerva Pentti Kanerva (born 1937) is a Finnish-born American neuroscientist and the originator of the sparse distributed memory model. He is responsible for relating…
- Distributional semantics Distributional semantics is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between…
- Word embedding In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is…
- Random projection In mathematics and statistics, random projection is a technique used to reduce the dimensionality of a set of points which lie in Euclidean space. According…
- Locality-sensitive hashing In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability…
- Dimensionality reduction Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the…
- Sparse distributed memory Sparse distributed memory (SDM) is a mathematical model of human long-term memory introduced by Pentti Kanerva in 1988 while he was at NASA Ames Research…
- Johnson–Lindenstrauss lemma In mathematics, the Johnson–Lindenstrauss lemma is a result named after William B. Johnson and Joram Lindenstrauss concerning low-distortion embeddings…