Aiko asano 浅野愛子 銀の妖精 - silver doll -

There are all kinds of terrible things that happen as the dimensionality of your descriptor vectors rises. One obvious one is that as the dimensionality rises, both the time and space complexity of dealing with these vectors rises, often exponentially. Another issue is that as dimensionality rises, the amount of samples needed to draw useful conclusions from that data also rises steeply. Another way of phrasing that is with a fixed number of samples, the usefulness of each dimension diminishes. Finally, as the dimensionality rises, your points all tend to start becoming equidistant to each other, making it difficult to draw solid conclusions from them. The umbrella term that covers all these adverse effects of high dimensionality is “the curse of dimensionality.”
Source : https:///scripted-updates/nlp-hacking-in-python
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Aiko Asano 浅野愛子 銀の妖精 - Silver Doll -