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514:"training". I don't think that WP articles are meant to be understood only by experts in their field. I've taken college physics and mathematics, and the only word familiar to me is "backpropagation", which is a term I recall from learning about neural networks. Does "training" have to do with data, or with neural networks that recognize data?
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To put this another way, the "dimensionality" of data does not have an obvious meaning, unless it refers to the use of indices to address data stored in an array, since an array has a dimension, which is the number of its indices. But does data have an inherent dimensionality that has nothing to do
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The lead paragraph refers principally to high and low dimension data, without any definition or examples. Even though I am a retired software engineer with 40 years of experience, I can only guess at what that might mean. A clear definition and some simple examples right up front would do much to
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Dimensional is an adjective modifying the noun "reduction". "Dimension" and "dimensionality" are both nouns. Similarly, one says "blue truck", where "blue" is the adjective modifying the noun "truck." "Blueness" is a noun-ified form of the word, which we could use as "reducing the blueness of the
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The article does say, "The training of deep encoders is typically performed using a greedy layer-wise pre-training (e.g., using a stack of restricted
Boltzmann machines) that is followed by a finetuning stage based on backpropagation," without defining "deep encoders", "Boltzmann machines", or
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with the fact that it may be stored in an array as opposed to some other method, such as a hologram? This article just confuses me, and seems to be written for someone already very familiar with its subject matter.
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The article needs to explain "training" better. To a typical intelligent person, "training" has nothing to do with data: if I said, "the data is 1, 2, and 3", how and why is this data to be trained?
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I looked at your link, and it simply appears to be a list of papers. Not sure how this relates to any bias in the article. Too bad you didn't use more words back there in 2006.
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Why is this called
Dimensionality Reduction? It should be called Dimension Reduction. Calling it "Dimensionality Reduction" is like overconjucationalizifying a word.
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the introduction, if not more, should probably be tailored more to those unfamiliar with the topic.
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on
Knowledge. If you would like to participate, please visit the project page, where you can join
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Nearest
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for additional information. I made the following changes:
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Why is there so much emphasis on using K-NN after PCA?
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truck". Saying "blueness truck" sounds wrong at best.
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make this article more widely useful, in my opinion.
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JMLR Special Issue on
Variable and Feature Selection
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