ReviewEntropy (Basel, Switzerland)2020
High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality.
Review in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Improving Voice Spoofing Detection Through Extensive Analysis of Multicepstral Feature Reduction.Sensors (Basel, Switzerland) · 2025Article
- High-performing neural network models of visual cortex benefit from high latent dimensionality.PLoS computational biology · 2024Article
- Entropy as a High-Level Feature for XAI-Based Early Plant Stress Detection.Entropy (Basel, Switzerland) · 2022Article
- Editorial: Toward and beyond human-level AI, volume II.Frontiers in neurorobotics · 2022Article
- Toward Reflective Spiking Neural Networks Exploiting Memristive Devices.Frontiers in computational neuroscience · 2022Review
- Estimating the dimensionality of the manifold underlying multi-electrode neural recordings.PLoS computational biology · 2021Article
- High-Dimensional Separability for One- and Few-Shot Learning.Entropy (Basel, Switzerland) · 2021Article
- Linear and Fisher Separability of Random Points in theEntropy (Basel, Switzerland) · 2020Article
- Fractional Norms and Quasinorms Do Not Help to Overcome the Curse of Dimensionality.Entropy (Basel, Switzerland) · 2020Article
- Universal principles justify the existence of concept cells.Scientific reports · 2020Article
- Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations.Frontiers in neurorobotics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning. The well-known phenomenon of the "curse of dimensionality" states: many problems become exponentially difficult in high dimensions. Recently, the other side of the coin, the "blessing of dimensionality", has attracted much attention. It turns out that generic high-dimensional datasets exhibit fairly simple geometric properties. Thus, there is a fundamental tradeoff between complexity and simplicity in high dimensional spaces. Here we present a brief explanatory review of recent ideas, results and hypotheses about the blessing of dimensionality and related simplifying effects relevant to machine learning and neuroscience.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.