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RESOURCES
[1] The curse of dimensionality means different things over time: odsc.medium.com/confronting-the-curse-of-dimensionality-5bcf2998b30d
[2] Effects of dimensionality on models: stats.stackexchange.com/questions/186184/does-dimensionality-curse-effect-some-models-more-than-others
[3] Deep Dive: towardsdatascience.com/high-dimensional-learning-ea6131785802
[4] Curse of Dimensionality minus the Jargon: towardsdatascience.com/the-curse-of-dimensionality-minus-the-curse-of-jargon-520da109fc87
[5] Paper that shows distance metrics in much higher dimensions are meaningless as you can’t distinguish between close points and points further away: www.researchgate.net/publication/30013021_On_the_Surprising_Behavior_of_Distance_Metric_in_High-Dimensional_Space
[6] Neural Networks are weird. Here is a paper that suggests neural networks can skirt the curse of dimensionality: arxiv.org/pdf/2105.04026.pdf
[7] Another strange case: openreview.net/pdf?id=KAV7BDCcN6
[8] Curse of Dimensionality in Word Embeddings: kikaben.com/word-embedding-lookup/
[9] Curse of Dimensionality on Linear Regression: stats.stackexchange.com/questions/243446/what-are-the-implications-of-the-curse-of-dimensionality-for-ordinary-least-squa
[10] K-Nearest Neighbors & how to deal with dimensionality: towardsdatascience.com/k-nearest-neighbors-and-the-curse-of-dimensionality-e39d10a6105d
[11] Udacity video on dimensionality: kzread.info/dash/bejne/ga2ExcSemdrWo5s.html
[12] Surprising behavior of distance metrics in high dimensions: towardsdatascience.com/the-surprising-behaviour-of-distance-metrics-in-high-dimensions-c2cb72779ea6
[13] Simulating the curse: stackoverflow.com/questions/69247773/generating-a-curse-of-dimensionality-chart
[14] Why Euclidian distances aren’t good for sparse data: stats.stackexchange.com/questions/29627/euclidean-distance-is-usually-not-good-for-sparse-data-and-more-general-case/29647#29647
[15] Advantages and disadvantages of K Means: developers.google.com/machine-learning/clustering/algorithm/advantages-disadvantages
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