When projecting data with UMAP, should I use only the samples I need projected or the entire dataset?One-Class discriminatory classification with imbalanced, heterogenous Negative background?Binary classification model for sparse / biased dataShould I use epochs > 1 when training data is unlimited?Purpose of visualizing high dimensional data?Feature selection - QR code localizationWhen should we consider a dataset as imbalanced?What are 2D dimensionality reduction algorithms good for?Which tool should I use for combining this large dataset?When should I normalize data?I have limited samples for one class, unlimited samples for the other class. Need to balance?
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When projecting data with UMAP, should I use only the samples I need projected or the entire dataset?
One-Class discriminatory classification with imbalanced, heterogenous Negative background?Binary classification model for sparse / biased dataShould I use epochs > 1 when training data is unlimited?Purpose of visualizing high dimensional data?Feature selection - QR code localizationWhen should we consider a dataset as imbalanced?What are 2D dimensionality reduction algorithms good for?Which tool should I use for combining this large dataset?When should I normalize data?I have limited samples for one class, unlimited samples for the other class. Need to balance?
$begingroup$
I have a neural network that maps my data samples to a 64-dimensional embedding. I wish to visualize a few of these embeddings (between 30 and 600) through a 2-dimensional projection, and I plan to use umap to do that. Would providing more embeddings sampled from the dataset along with the ones I want to project help the algorithm to identify the manifold and improve the quality of projection?
machine-learning dataset dimensionality-reduction
asked Jan 1 at 13:24
Le FriteLe Frite
62
$endgroup$
add a comment |
$begingroup$
I have a neural network that maps my data samples to a 64-dimensional embedding. I wish to visualize a few of these embeddings (between 30 and 600) through a 2-dimensional projection, and I plan to use umap to do that. Would providing more embeddings sampled from the dataset along with the ones I want to project help the algorithm to identify the manifold and improve the quality of projection?
machine-learning dataset dimensionality-reduction
asked Jan 1 at 13:24
Le FriteLe Frite
62
$endgroup$
$begingroup$
Let me see if I understand you correctly, you learn a 64-dimensional embedding from your data using a neural network and you want to reduce it to a 2-dimensional space to visualize using UMAP? What prohibits you of providing more embeddings sampled from the dataset? Intuitively speaking, it sounds to me having more data points would make a difference in the manifold (though I have no proof of it!; have to dig more).
$endgroup$
– Majid Mortazavi
Jan 1 at 15:01
$begingroup$
It's merely a matter of performance. I'd like to have an idea of the order of magnitude of the number of samples needed to have a meaningful projection. More samples for UMAP means I need to forward more samples through the network and that UMAP will train for longer. With less than ~50 samples, I can run UMAP in my mainloop and not worry much about the impact. With more than that, I'll need to run a secondary thread.
$endgroup$
– Le Frite
Jan 1 at 15:50
add a comment |
$begingroup$
I have a neural network that maps my data samples to a 64-dimensional embedding. I wish to visualize a few of these embeddings (between 30 and 600) through a 2-dimensional projection, and I plan to use umap to do that. Would providing more embeddings sampled from the dataset along with the ones I want to project help the algorithm to identify the manifold and improve the quality of projection?
machine-learning dataset dimensionality-reduction
asked Jan 1 at 13:24
Le FriteLe Frite
62
$endgroup$
I have a neural network that maps my data samples to a 64-dimensional embedding. I wish to visualize a few of these embeddings (between 30 and 600) through a 2-dimensional projection, and I plan to use umap to do that. Would providing more embeddings sampled from the dataset along with the ones I want to project help the algorithm to identify the manifold and improve the quality of projection?
machine-learning dataset dimensionality-reduction
machine-learning dataset dimensionality-reduction
asked Jan 1 at 13:24
Le FriteLe Frite
62
asked Jan 1 at 13:24
Le FriteLe Frite
62
asked Jan 1 at 13:24
Le FriteLe Frite
62
asked Jan 1 at 13:24
Le FriteLe Frite
62
asked Jan 1 at 13:24
Le FriteLe Frite
62
62
$begingroup$
Let me see if I understand you correctly, you learn a 64-dimensional embedding from your data using a neural network and you want to reduce it to a 2-dimensional space to visualize using UMAP? What prohibits you of providing more embeddings sampled from the dataset? Intuitively speaking, it sounds to me having more data points would make a difference in the manifold (though I have no proof of it!; have to dig more).
$endgroup$
– Majid Mortazavi
Jan 1 at 15:01
$begingroup$
It's merely a matter of performance. I'd like to have an idea of the order of magnitude of the number of samples needed to have a meaningful projection. More samples for UMAP means I need to forward more samples through the network and that UMAP will train for longer. With less than ~50 samples, I can run UMAP in my mainloop and not worry much about the impact. With more than that, I'll need to run a secondary thread.
$endgroup$
– Le Frite
Jan 1 at 15:50
add a comment |
$begingroup$
Let me see if I understand you correctly, you learn a 64-dimensional embedding from your data using a neural network and you want to reduce it to a 2-dimensional space to visualize using UMAP? What prohibits you of providing more embeddings sampled from the dataset? Intuitively speaking, it sounds to me having more data points would make a difference in the manifold (though I have no proof of it!; have to dig more).
$endgroup$
– Majid Mortazavi
Jan 1 at 15:01
$begingroup$
It's merely a matter of performance. I'd like to have an idea of the order of magnitude of the number of samples needed to have a meaningful projection. More samples for UMAP means I need to forward more samples through the network and that UMAP will train for longer. With less than ~50 samples, I can run UMAP in my mainloop and not worry much about the impact. With more than that, I'll need to run a secondary thread.
$endgroup$
– Le Frite
Jan 1 at 15:50
$begingroup$
Let me see if I understand you correctly, you learn a 64-dimensional embedding from your data using a neural network and you want to reduce it to a 2-dimensional space to visualize using UMAP? What prohibits you of providing more embeddings sampled from the dataset? Intuitively speaking, it sounds to me having more data points would make a difference in the manifold (though I have no proof of it!; have to dig more).
$endgroup$
– Majid Mortazavi
Jan 1 at 15:01
$begingroup$
Let me see if I understand you correctly, you learn a 64-dimensional embedding from your data using a neural network and you want to reduce it to a 2-dimensional space to visualize using UMAP? What prohibits you of providing more embeddings sampled from the dataset? Intuitively speaking, it sounds to me having more data points would make a difference in the manifold (though I have no proof of it!; have to dig more).
$endgroup$
– Majid Mortazavi
Jan 1 at 15:01
$begingroup$
It's merely a matter of performance. I'd like to have an idea of the order of magnitude of the number of samples needed to have a meaningful projection. More samples for UMAP means I need to forward more samples through the network and that UMAP will train for longer. With less than ~50 samples, I can run UMAP in my mainloop and not worry much about the impact. With more than that, I'll need to run a secondary thread.
$endgroup$
– Le Frite
Jan 1 at 15:50
$begingroup$
It's merely a matter of performance. I'd like to have an idea of the order of magnitude of the number of samples needed to have a meaningful projection. More samples for UMAP means I need to forward more samples through the network and that UMAP will train for longer. With less than ~50 samples, I can run UMAP in my mainloop and not worry much about the impact. With more than that, I'll need to run a secondary thread.
$endgroup$
– Le Frite
Jan 1 at 15:50
add a comment |
1 Answer
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$begingroup$
Yes, more data will improve the quality of the embedding UMAP can produce. While UMAP is somewhat robust/stable under subsampling in general you will get significantly better results with more data. It is also worth noting that most UMAP implementations are not designed for very small datasets (they make some optimization choices that assume a a reasonable dataset size). In practice it is probably best not to use UMAP with less than 100 or so data samples.
answered 12 mins ago
Leland McInnesLeland McInnes
1513
$endgroup$
add a comment |
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1 Answer
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$begingroup$
Yes, more data will improve the quality of the embedding UMAP can produce. While UMAP is somewhat robust/stable under subsampling in general you will get significantly better results with more data. It is also worth noting that most UMAP implementations are not designed for very small datasets (they make some optimization choices that assume a a reasonable dataset size). In practice it is probably best not to use UMAP with less than 100 or so data samples.
answered 12 mins ago
Leland McInnesLeland McInnes
1513
$endgroup$
add a comment |
$begingroup$
Yes, more data will improve the quality of the embedding UMAP can produce. While UMAP is somewhat robust/stable under subsampling in general you will get significantly better results with more data. It is also worth noting that most UMAP implementations are not designed for very small datasets (they make some optimization choices that assume a a reasonable dataset size). In practice it is probably best not to use UMAP with less than 100 or so data samples.
answered 12 mins ago
Leland McInnesLeland McInnes
1513
$endgroup$
add a comment |
$begingroup$
Yes, more data will improve the quality of the embedding UMAP can produce. While UMAP is somewhat robust/stable under subsampling in general you will get significantly better results with more data. It is also worth noting that most UMAP implementations are not designed for very small datasets (they make some optimization choices that assume a a reasonable dataset size). In practice it is probably best not to use UMAP with less than 100 or so data samples.
answered 12 mins ago
Leland McInnesLeland McInnes
1513
$endgroup$
Yes, more data will improve the quality of the embedding UMAP can produce. While UMAP is somewhat robust/stable under subsampling in general you will get significantly better results with more data. It is also worth noting that most UMAP implementations are not designed for very small datasets (they make some optimization choices that assume a a reasonable dataset size). In practice it is probably best not to use UMAP with less than 100 or so data samples.
answered 12 mins ago
Leland McInnesLeland McInnes
1513
answered 12 mins ago
Leland McInnesLeland McInnes
1513
answered 12 mins ago
Leland McInnesLeland McInnes
1513
answered 12 mins ago
Leland McInnesLeland McInnes
1513
1513
add a comment |
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$begingroup$
Let me see if I understand you correctly, you learn a 64-dimensional embedding from your data using a neural network and you want to reduce it to a 2-dimensional space to visualize using UMAP? What prohibits you of providing more embeddings sampled from the dataset? Intuitively speaking, it sounds to me having more data points would make a difference in the manifold (though I have no proof of it!; have to dig more).
$endgroup$
– Majid Mortazavi
Jan 1 at 15:01
$begingroup$
It's merely a matter of performance. I'd like to have an idea of the order of magnitude of the number of samples needed to have a meaningful projection. More samples for UMAP means I need to forward more samples through the network and that UMAP will train for longer. With less than ~50 samples, I can run UMAP in my mainloop and not worry much about the impact. With more than that, I'll need to run a secondary thread.
$endgroup$
– Le Frite
Jan 1 at 15:50