| How to compress the count vector |
| output |
| vector |
| 2000
reconstructed counts |
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| • | We train the
neural |
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| network to
reproduce its |
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| input vector as
its output |
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| • | This forces it to |
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| compress as much |
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| information as
possible |
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| into the 10
numbers in |
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| the central
bottleneck. |
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| • | These 10 numbers
are |
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| then a good way
to |
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| compare
documents. |
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| 500 neurons |
| 250 neurons |
| 10 |
| 250 neurons |
| 500 neurons |
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|
| input |
|
| vector |
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| 2000
word counts |