Suitable Autoencoder for Activity Recognition dataset Feature Extraction
$begingroup$
Dataset: I have text data representing sensor outputs:
1458996986002; 11.43,-15.86,11.20,508.26; -1.59,-0.22,6.17,40.68; 126.0,-150.9,-105.0,49671.81; Walk
1459002923002; 16.69,-12.68,13.96,634.65; -2.55,2.13,4.87,34.87; 126.0,-150.9,-105.0,49671.81; Walk
timestamp; acc_x,acc_y,acc_z; gyro_x,gyro_y,gyro_z; magn_x,magn_y,magn_z; ActivityName
Aim: I want to extract features from this text lines before feeding it to Recurrent Neural Network (GRU/LSTM). So aim is automatic feature extraction. Those extracted features (encoder network) will be used before Neural Network for activity recognition task (classification).
Question: Which Autoencoder (denoising, variational, sparse) is suitable for such dataset? Or should i prefer RBM? After choosing feature extraction method, how do i compare the output with input, since input is not 0s and 1s?
What i have read: I read that RBM is generative model, which, even if you give some similar input, it can generate similar correct output. And training autoencoder is said(1) to be easier(2) than RBM. On the other hand, variational autoencoder can do something similar (generative). First question would be is having generative ability has any advantage for above problem, because at the end of pretraining i would just use encoder part and throw decoder layers? If no, denoising autoencoder seems right apporach imo.
Last thing i know, to force network to learn important features from data (instead of memorizing), you can choose following methods: 1) use sandwich like layers, 2) add noise to input 3) regularize autoencoder=make only some nodes active at the same time
neural-network classification feature-extraction autoencoder rbm
asked Mar 19 '18 at 18:20
Jemshit IskenderovJemshit Iskenderov
1012
$endgroup$
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Dataset: I have text data representing sensor outputs:
1458996986002; 11.43,-15.86,11.20,508.26; -1.59,-0.22,6.17,40.68; 126.0,-150.9,-105.0,49671.81; Walk
1459002923002; 16.69,-12.68,13.96,634.65; -2.55,2.13,4.87,34.87; 126.0,-150.9,-105.0,49671.81; Walk
timestamp; acc_x,acc_y,acc_z; gyro_x,gyro_y,gyro_z; magn_x,magn_y,magn_z; ActivityName
Aim: I want to extract features from this text lines before feeding it to Recurrent Neural Network (GRU/LSTM). So aim is automatic feature extraction. Those extracted features (encoder network) will be used before Neural Network for activity recognition task (classification).
Question: Which Autoencoder (denoising, variational, sparse) is suitable for such dataset? Or should i prefer RBM? After choosing feature extraction method, how do i compare the output with input, since input is not 0s and 1s?
What i have read: I read that RBM is generative model, which, even if you give some similar input, it can generate similar correct output. And training autoencoder is said(1) to be easier(2) than RBM. On the other hand, variational autoencoder can do something similar (generative). First question would be is having generative ability has any advantage for above problem, because at the end of pretraining i would just use encoder part and throw decoder layers? If no, denoising autoencoder seems right apporach imo.
Last thing i know, to force network to learn important features from data (instead of memorizing), you can choose following methods: 1) use sandwich like layers, 2) add noise to input 3) regularize autoencoder=make only some nodes active at the same time
neural-network classification feature-extraction autoencoder rbm
asked Mar 19 '18 at 18:20
Jemshit IskenderovJemshit Iskenderov
1012
$endgroup$
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
Also i will be providing about 100 readings at once (based on window size like 2seconds) to autoencoder
$endgroup$
– Jemshit Iskenderov
Mar 21 '18 at 6:42
add a comment |
$begingroup$
Dataset: I have text data representing sensor outputs:
1458996986002; 11.43,-15.86,11.20,508.26; -1.59,-0.22,6.17,40.68; 126.0,-150.9,-105.0,49671.81; Walk
1459002923002; 16.69,-12.68,13.96,634.65; -2.55,2.13,4.87,34.87; 126.0,-150.9,-105.0,49671.81; Walk
timestamp; acc_x,acc_y,acc_z; gyro_x,gyro_y,gyro_z; magn_x,magn_y,magn_z; ActivityName
Aim: I want to extract features from this text lines before feeding it to Recurrent Neural Network (GRU/LSTM). So aim is automatic feature extraction. Those extracted features (encoder network) will be used before Neural Network for activity recognition task (classification).
Question: Which Autoencoder (denoising, variational, sparse) is suitable for such dataset? Or should i prefer RBM? After choosing feature extraction method, how do i compare the output with input, since input is not 0s and 1s?
What i have read: I read that RBM is generative model, which, even if you give some similar input, it can generate similar correct output. And training autoencoder is said(1) to be easier(2) than RBM. On the other hand, variational autoencoder can do something similar (generative). First question would be is having generative ability has any advantage for above problem, because at the end of pretraining i would just use encoder part and throw decoder layers? If no, denoising autoencoder seems right apporach imo.
Last thing i know, to force network to learn important features from data (instead of memorizing), you can choose following methods: 1) use sandwich like layers, 2) add noise to input 3) regularize autoencoder=make only some nodes active at the same time
neural-network classification feature-extraction autoencoder rbm
asked Mar 19 '18 at 18:20
Jemshit IskenderovJemshit Iskenderov
1012
$endgroup$
Dataset: I have text data representing sensor outputs:
1458996986002; 11.43,-15.86,11.20,508.26; -1.59,-0.22,6.17,40.68; 126.0,-150.9,-105.0,49671.81; Walk
1459002923002; 16.69,-12.68,13.96,634.65; -2.55,2.13,4.87,34.87; 126.0,-150.9,-105.0,49671.81; Walk
timestamp; acc_x,acc_y,acc_z; gyro_x,gyro_y,gyro_z; magn_x,magn_y,magn_z; ActivityName
Aim: I want to extract features from this text lines before feeding it to Recurrent Neural Network (GRU/LSTM). So aim is automatic feature extraction. Those extracted features (encoder network) will be used before Neural Network for activity recognition task (classification).
Question: Which Autoencoder (denoising, variational, sparse) is suitable for such dataset? Or should i prefer RBM? After choosing feature extraction method, how do i compare the output with input, since input is not 0s and 1s?
What i have read: I read that RBM is generative model, which, even if you give some similar input, it can generate similar correct output. And training autoencoder is said(1) to be easier(2) than RBM. On the other hand, variational autoencoder can do something similar (generative). First question would be is having generative ability has any advantage for above problem, because at the end of pretraining i would just use encoder part and throw decoder layers? If no, denoising autoencoder seems right apporach imo.
Last thing i know, to force network to learn important features from data (instead of memorizing), you can choose following methods: 1) use sandwich like layers, 2) add noise to input 3) regularize autoencoder=make only some nodes active at the same time
neural-network classification feature-extraction autoencoder rbm
neural-network classification feature-extraction autoencoder rbm
asked Mar 19 '18 at 18:20
Jemshit IskenderovJemshit Iskenderov
1012
asked Mar 19 '18 at 18:20
Jemshit IskenderovJemshit Iskenderov
1012
asked Mar 19 '18 at 18:20
Jemshit IskenderovJemshit Iskenderov
1012
asked Mar 19 '18 at 18:20
Jemshit IskenderovJemshit Iskenderov
1012
asked Mar 19 '18 at 18:20
Jemshit IskenderovJemshit Iskenderov
1012
1012
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
Also i will be providing about 100 readings at once (based on window size like 2seconds) to autoencoder
$endgroup$
– Jemshit Iskenderov
Mar 21 '18 at 6:42
add a comment |
$begingroup$
Also i will be providing about 100 readings at once (based on window size like 2seconds) to autoencoder
$endgroup$
– Jemshit Iskenderov
Mar 21 '18 at 6:42
$begingroup$
Also i will be providing about 100 readings at once (based on window size like 2seconds) to autoencoder
$endgroup$
– Jemshit Iskenderov
Mar 21 '18 at 6:42
$begingroup$
Also i will be providing about 100 readings at once (based on window size like 2seconds) to autoencoder
$endgroup$
– Jemshit Iskenderov
Mar 21 '18 at 6:42
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Because you want to do activity recognition based on time windows using deep learning models with autoencoders I would strongly suggest to have a look at the following articles:
https://www.researchgate.net/publication/323019783_An_Effective_Deep_Autoencoder_Approach_for_Online_Smartphone-Based_Human_Activity_Recognition
It gives an overview of a custom CNN/RNN model which might give an initial idea of how to proceed for the specific task of AR.
and
https://ieeexplore.ieee.org/document/8422895
It gives a general idea of how to reconstruct sensor data with semantic meanings before feeding the constructed data features to another model.
answered Dec 19 '18 at 10:09
hristijan_dshristijan_ds
863
$endgroup$
add a comment |
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1 Answer
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$begingroup$
Because you want to do activity recognition based on time windows using deep learning models with autoencoders I would strongly suggest to have a look at the following articles:
https://www.researchgate.net/publication/323019783_An_Effective_Deep_Autoencoder_Approach_for_Online_Smartphone-Based_Human_Activity_Recognition
It gives an overview of a custom CNN/RNN model which might give an initial idea of how to proceed for the specific task of AR.
and
https://ieeexplore.ieee.org/document/8422895
It gives a general idea of how to reconstruct sensor data with semantic meanings before feeding the constructed data features to another model.
answered Dec 19 '18 at 10:09
hristijan_dshristijan_ds
863
$endgroup$
add a comment |
$begingroup$
Because you want to do activity recognition based on time windows using deep learning models with autoencoders I would strongly suggest to have a look at the following articles:
https://www.researchgate.net/publication/323019783_An_Effective_Deep_Autoencoder_Approach_for_Online_Smartphone-Based_Human_Activity_Recognition
It gives an overview of a custom CNN/RNN model which might give an initial idea of how to proceed for the specific task of AR.
and
https://ieeexplore.ieee.org/document/8422895
It gives a general idea of how to reconstruct sensor data with semantic meanings before feeding the constructed data features to another model.
answered Dec 19 '18 at 10:09
hristijan_dshristijan_ds
863
$endgroup$
add a comment |
$begingroup$
Because you want to do activity recognition based on time windows using deep learning models with autoencoders I would strongly suggest to have a look at the following articles:
https://www.researchgate.net/publication/323019783_An_Effective_Deep_Autoencoder_Approach_for_Online_Smartphone-Based_Human_Activity_Recognition
It gives an overview of a custom CNN/RNN model which might give an initial idea of how to proceed for the specific task of AR.
and
https://ieeexplore.ieee.org/document/8422895
It gives a general idea of how to reconstruct sensor data with semantic meanings before feeding the constructed data features to another model.
answered Dec 19 '18 at 10:09
hristijan_dshristijan_ds
863
$endgroup$
Because you want to do activity recognition based on time windows using deep learning models with autoencoders I would strongly suggest to have a look at the following articles:
https://www.researchgate.net/publication/323019783_An_Effective_Deep_Autoencoder_Approach_for_Online_Smartphone-Based_Human_Activity_Recognition
It gives an overview of a custom CNN/RNN model which might give an initial idea of how to proceed for the specific task of AR.
and
https://ieeexplore.ieee.org/document/8422895
It gives a general idea of how to reconstruct sensor data with semantic meanings before feeding the constructed data features to another model.
answered Dec 19 '18 at 10:09
hristijan_dshristijan_ds
863
answered Dec 19 '18 at 10:09
hristijan_dshristijan_ds
863
answered Dec 19 '18 at 10:09
hristijan_dshristijan_ds
863
answered Dec 19 '18 at 10:09
hristijan_dshristijan_ds
863
863
add a comment |
add a comment |
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$begingroup$
Also i will be providing about 100 readings at once (based on window size like 2seconds) to autoencoder
$endgroup$
– Jemshit Iskenderov
Mar 21 '18 at 6:42