How to compare the output of Self-organizing maps?
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I am trying to simultaneously cluster and visualize text documents using Self-organizing maps. Since text documents can be represented in various ways (vector space model, GloVe etc), I am trying to figure out how to tell which representation generates the best map. Measures like Quantization error etc., determine the goodness of the map given a dataset. However, they are not useful for quantitatively telling which representation gives a better output.
Is there a quantitative measure to compare the maps generated using different representations (for example, Tf-idf and GloVe) and tell for which representation the output is better?
machine-learning neural-network clustering
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I am trying to simultaneously cluster and visualize text documents using Self-organizing maps. Since text documents can be represented in various ways (vector space model, GloVe etc), I am trying to figure out how to tell which representation generates the best map. Measures like Quantization error etc., determine the goodness of the map given a dataset. However, they are not useful for quantitatively telling which representation gives a better output.
Is there a quantitative measure to compare the maps generated using different representations (for example, Tf-idf and GloVe) and tell for which representation the output is better?
machine-learning neural-network clustering
asked 10 mins ago
user66305user66305
1
New contributor
user66305 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am trying to simultaneously cluster and visualize text documents using Self-organizing maps. Since text documents can be represented in various ways (vector space model, GloVe etc), I am trying to figure out how to tell which representation generates the best map. Measures like Quantization error etc., determine the goodness of the map given a dataset. However, they are not useful for quantitatively telling which representation gives a better output.
Is there a quantitative measure to compare the maps generated using different representations (for example, Tf-idf and GloVe) and tell for which representation the output is better?
machine-learning neural-network clustering
asked 10 mins ago
user66305user66305
1
New contributor
user66305 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am trying to simultaneously cluster and visualize text documents using Self-organizing maps. Since text documents can be represented in various ways (vector space model, GloVe etc), I am trying to figure out how to tell which representation generates the best map. Measures like Quantization error etc., determine the goodness of the map given a dataset. However, they are not useful for quantitatively telling which representation gives a better output.
Is there a quantitative measure to compare the maps generated using different representations (for example, Tf-idf and GloVe) and tell for which representation the output is better?
machine-learning neural-network clustering
machine-learning neural-network clustering
asked 10 mins ago
user66305user66305
1
New contributor
user66305 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 mins ago
user66305user66305
1
New contributor
user66305 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 mins ago
user66305user66305
1
New contributor
user66305 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 mins ago
user66305user66305
1
asked 10 mins ago
user66305user66305
1
1
New contributor
user66305 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user66305 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
user66305 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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