Will the features in the image (edge, color, etc.. ) impacts on the performance of the spherical k-means?
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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 10 mins ago
Kreedz ZhenKreedz Zhen
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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 10 mins ago
Kreedz ZhenKreedz Zhen
1
New contributor
Kreedz Zhen 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 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 10 mins ago
Kreedz ZhenKreedz Zhen
1
New contributor
Kreedz Zhen 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 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 10 mins ago
Kreedz ZhenKreedz Zhen
1
New contributor
Kreedz Zhen 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
Kreedz ZhenKreedz Zhen
1
New contributor
Kreedz Zhen 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
Kreedz ZhenKreedz Zhen
1
New contributor
Kreedz Zhen 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
Kreedz ZhenKreedz Zhen
1
asked 10 mins ago
Kreedz ZhenKreedz Zhen
1
1
New contributor
Kreedz Zhen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Kreedz Zhen is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Kreedz Zhen 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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