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How to combine features with different temporal scale in machine learning
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsFeature importance varying a lot using same data with same featuresIdentifying important interactions between features using machine learninghow to combine count and rate features in clusteringHow to combine heterogeneous image features extracted with different algorithms for similar image retrieval?Determine useful features for machine learning modelHow to handle large number of features in machine learning?Combine multiple features for text classificationFeeding machine learning model with different matrixHow can I improve a machine learning model?How exactly do I extract the important features from strings for machine learning?
$begingroup$
We have various types of data features with different temporal scale. For example, some of them describe the state per second while others may describe the state per day or per month from another aspect. The former features are dense on the time scale and latter features are sparse. Simply concatenate them into one feature vector seems not proper. Is there any typical method in machine learning can handle with problem ?
machine-learning feature-selection feature-engineering
asked 37 mins ago
JunjieChenJunjieChen
534
$endgroup$
add a comment |
$begingroup$
We have various types of data features with different temporal scale. For example, some of them describe the state per second while others may describe the state per day or per month from another aspect. The former features are dense on the time scale and latter features are sparse. Simply concatenate them into one feature vector seems not proper. Is there any typical method in machine learning can handle with problem ?
machine-learning feature-selection feature-engineering
asked 37 mins ago
JunjieChenJunjieChen
534
$endgroup$
add a comment |
$begingroup$
We have various types of data features with different temporal scale. For example, some of them describe the state per second while others may describe the state per day or per month from another aspect. The former features are dense on the time scale and latter features are sparse. Simply concatenate them into one feature vector seems not proper. Is there any typical method in machine learning can handle with problem ?
machine-learning feature-selection feature-engineering
asked 37 mins ago
JunjieChenJunjieChen
534
$endgroup$
We have various types of data features with different temporal scale. For example, some of them describe the state per second while others may describe the state per day or per month from another aspect. The former features are dense on the time scale and latter features are sparse. Simply concatenate them into one feature vector seems not proper. Is there any typical method in machine learning can handle with problem ?
machine-learning feature-selection feature-engineering
machine-learning feature-selection feature-engineering
asked 37 mins ago
JunjieChenJunjieChen
534
asked 37 mins ago
JunjieChenJunjieChen
534
asked 37 mins ago
JunjieChenJunjieChen
534
asked 37 mins ago
JunjieChenJunjieChen
534
asked 37 mins ago
JunjieChenJunjieChen
534
534
add a comment |
add a comment |
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