Will the features in the image (edge, color, etc.. ) impacts on the performance of the spherical k-means? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsConstrained k-means algorithms in R (must-link constraints)Need help with how to diagnose training and validation metricscalculate distance between each data point of a cluster to their respective cluster centroidsIs deduction, genetic programming, PCA, or clustering machine learning according to Tom Mitchells definition?How to implement one vs rest classifier in a multiclass classification problem?Are there cases where tree based algorithms can do better than neural networks?How standardizing and/or log transformation affect prediction result in machine learning modelsGetting negative r2_score with new set of dimensionsBest way to implement drone detection?k modes: optimal k
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Will the features in the image (edge, color, etc.. ) impacts on the performance of the spherical k-means?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsConstrained k-means algorithms in R (must-link constraints)Need help with how to diagnose training and validation metricscalculate distance between each data point of a cluster to their respective cluster centroidsIs deduction, genetic programming, PCA, or clustering machine learning according to Tom Mitchells definition?How to implement one vs rest classifier in a multiclass classification problem?Are there cases where tree based algorithms can do better than neural networks?How standardizing and/or log transformation affect prediction result in machine learning modelsGetting negative r2_score with new set of dimensionsBest way to implement drone detection?k modes: optimal k
$begingroup$
I am very new in Machine learning, I recently implemented the spherical k-means, but finally I found a interesting point from the result. I used four datasets, they are minst, cifar-10, fashion-minst, and svhn. I was following the paper Learning Feature Representations with K-means (Coates & Ng, 2012) https://www-cs.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf. I did not finish the deep learning yet.
See the following image, I tried the max pooling and average pooling to see if there any difference, but I found an interesting point, the dataset with the clear feature (edge, color, etc. of the object in the image ) has the high performance, the dataset without clear feature has low performance.
See the four pictures on the bottom, I extracted one image from each dataset. I ordered them according the performance. I tried search online, but I did not get any useful information about my question.
I am not sure whether the point is correct or not. Could any one please explain this to me. Thanks!
machine-learning k-means
asked Apr 5 at 1:10
Kreedz ZhenKreedz Zhen
1
$endgroup$
add a comment |
$begingroup$
I am very new in Machine learning, I recently implemented the spherical k-means, but finally I found a interesting point from the result. I used four datasets, they are minst, cifar-10, fashion-minst, and svhn. I was following the paper Learning Feature Representations with K-means (Coates & Ng, 2012) https://www-cs.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf. I did not finish the deep learning yet.
See the following image, I tried the max pooling and average pooling to see if there any difference, but I found an interesting point, the dataset with the clear feature (edge, color, etc. of the object in the image ) has the high performance, the dataset without clear feature has low performance.
See the four pictures on the bottom, I extracted one image from each dataset. I ordered them according the performance. I tried search online, but I did not get any useful information about my question.
I am not sure whether the point is correct or not. Could any one please explain this to me. Thanks!
machine-learning k-means
asked Apr 5 at 1:10
Kreedz ZhenKreedz Zhen
1
$endgroup$
add a comment |
$begingroup$
I am very new in Machine learning, I recently implemented the spherical k-means, but finally I found a interesting point from the result. I used four datasets, they are minst, cifar-10, fashion-minst, and svhn. I was following the paper Learning Feature Representations with K-means (Coates & Ng, 2012) https://www-cs.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf. I did not finish the deep learning yet.
See the following image, I tried the max pooling and average pooling to see if there any difference, but I found an interesting point, the dataset with the clear feature (edge, color, etc. of the object in the image ) has the high performance, the dataset without clear feature has low performance.
See the four pictures on the bottom, I extracted one image from each dataset. I ordered them according the performance. I tried search online, but I did not get any useful information about my question.
I am not sure whether the point is correct or not. Could any one please explain this to me. Thanks!
machine-learning k-means
asked Apr 5 at 1:10
Kreedz ZhenKreedz Zhen
1
$endgroup$
I am very new in Machine learning, I recently implemented the spherical k-means, but finally I found a interesting point from the result. I used four datasets, they are minst, cifar-10, fashion-minst, and svhn. I was following the paper Learning Feature Representations with K-means (Coates & Ng, 2012) https://www-cs.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf. I did not finish the deep learning yet.
See the following image, I tried the max pooling and average pooling to see if there any difference, but I found an interesting point, the dataset with the clear feature (edge, color, etc. of the object in the image ) has the high performance, the dataset without clear feature has low performance.
See the four pictures on the bottom, I extracted one image from each dataset. I ordered them according the performance. I tried search online, but I did not get any useful information about my question.
I am not sure whether the point is correct or not. Could any one please explain this to me. Thanks!
machine-learning k-means
machine-learning k-means
asked Apr 5 at 1:10
Kreedz ZhenKreedz Zhen
1
asked Apr 5 at 1:10
Kreedz ZhenKreedz Zhen
1
asked Apr 5 at 1:10
Kreedz ZhenKreedz Zhen
1
asked Apr 5 at 1:10
Kreedz ZhenKreedz Zhen
1
asked Apr 5 at 1:10
Kreedz ZhenKreedz Zhen
1
1
add a comment |
add a comment |
1 Answer
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$begingroup$
Yes it is true that in these images follow the trend you pointed, but that is not the only reason for the drop in accuracy:
Image 1 is pretty simple, and binary so it is easier samples, not because of clarity but of simplicity.
Image 2 is a bit more complex, but it is still binary and can be learned easily.
Image 3 is simple, but it is noisy and (as you pointed out) does not have strong features, since you're not dealing with CNNs you should pre-process this kind of dataset by binarization or image sharpening after noise removal (if you do it before you will amplify noise). This probably was taken with bad light conditions.
Image 4 this is really complex image, it is on color and until now I couldn't figure out what it is lol
Well, you are not wrong in your idea, but taking conclusions that way may mislead you on the future. There are other reasons (as pointed above) for your findings. So:
- Create a thesis (like you did)
- Test your thesis by trying to change the observed phenomena (e.g. pre-process images to find stronger features)
- Check if that improved your results, create a new thesis and repeat until satisfied with results
answered Apr 5 at 4:25
Pedro Henrique MonfortePedro Henrique Monforte
559218
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$begingroup$
Yes it is true that in these images follow the trend you pointed, but that is not the only reason for the drop in accuracy:
Image 1 is pretty simple, and binary so it is easier samples, not because of clarity but of simplicity.
Image 2 is a bit more complex, but it is still binary and can be learned easily.
Image 3 is simple, but it is noisy and (as you pointed out) does not have strong features, since you're not dealing with CNNs you should pre-process this kind of dataset by binarization or image sharpening after noise removal (if you do it before you will amplify noise). This probably was taken with bad light conditions.
Image 4 this is really complex image, it is on color and until now I couldn't figure out what it is lol
Well, you are not wrong in your idea, but taking conclusions that way may mislead you on the future. There are other reasons (as pointed above) for your findings. So:
- Create a thesis (like you did)
- Test your thesis by trying to change the observed phenomena (e.g. pre-process images to find stronger features)
- Check if that improved your results, create a new thesis and repeat until satisfied with results
answered Apr 5 at 4:25
Pedro Henrique MonfortePedro Henrique Monforte
559218
$endgroup$
add a comment |
$begingroup$
Yes it is true that in these images follow the trend you pointed, but that is not the only reason for the drop in accuracy:
Image 1 is pretty simple, and binary so it is easier samples, not because of clarity but of simplicity.
Image 2 is a bit more complex, but it is still binary and can be learned easily.
Image 3 is simple, but it is noisy and (as you pointed out) does not have strong features, since you're not dealing with CNNs you should pre-process this kind of dataset by binarization or image sharpening after noise removal (if you do it before you will amplify noise). This probably was taken with bad light conditions.
Image 4 this is really complex image, it is on color and until now I couldn't figure out what it is lol
Well, you are not wrong in your idea, but taking conclusions that way may mislead you on the future. There are other reasons (as pointed above) for your findings. So:
- Create a thesis (like you did)
- Test your thesis by trying to change the observed phenomena (e.g. pre-process images to find stronger features)
- Check if that improved your results, create a new thesis and repeat until satisfied with results
answered Apr 5 at 4:25
Pedro Henrique MonfortePedro Henrique Monforte
559218
$endgroup$
add a comment |
$begingroup$
Yes it is true that in these images follow the trend you pointed, but that is not the only reason for the drop in accuracy:
Image 1 is pretty simple, and binary so it is easier samples, not because of clarity but of simplicity.
Image 2 is a bit more complex, but it is still binary and can be learned easily.
Image 3 is simple, but it is noisy and (as you pointed out) does not have strong features, since you're not dealing with CNNs you should pre-process this kind of dataset by binarization or image sharpening after noise removal (if you do it before you will amplify noise). This probably was taken with bad light conditions.
Image 4 this is really complex image, it is on color and until now I couldn't figure out what it is lol
Well, you are not wrong in your idea, but taking conclusions that way may mislead you on the future. There are other reasons (as pointed above) for your findings. So:
- Create a thesis (like you did)
- Test your thesis by trying to change the observed phenomena (e.g. pre-process images to find stronger features)
- Check if that improved your results, create a new thesis and repeat until satisfied with results
answered Apr 5 at 4:25
Pedro Henrique MonfortePedro Henrique Monforte
559218
$endgroup$
Yes it is true that in these images follow the trend you pointed, but that is not the only reason for the drop in accuracy:
Image 1 is pretty simple, and binary so it is easier samples, not because of clarity but of simplicity.
Image 2 is a bit more complex, but it is still binary and can be learned easily.
Image 3 is simple, but it is noisy and (as you pointed out) does not have strong features, since you're not dealing with CNNs you should pre-process this kind of dataset by binarization or image sharpening after noise removal (if you do it before you will amplify noise). This probably was taken with bad light conditions.
Image 4 this is really complex image, it is on color and until now I couldn't figure out what it is lol
Well, you are not wrong in your idea, but taking conclusions that way may mislead you on the future. There are other reasons (as pointed above) for your findings. So:
- Create a thesis (like you did)
- Test your thesis by trying to change the observed phenomena (e.g. pre-process images to find stronger features)
- Check if that improved your results, create a new thesis and repeat until satisfied with results
answered Apr 5 at 4:25
Pedro Henrique MonfortePedro Henrique Monforte
559218
answered Apr 5 at 4:25
Pedro Henrique MonfortePedro Henrique Monforte
559218
answered Apr 5 at 4:25
Pedro Henrique MonfortePedro Henrique Monforte
559218
answered Apr 5 at 4:25
Pedro Henrique MonfortePedro Henrique Monforte
559218
559218
add a comment |
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