When projecting data with UMAP, should I use only the samples I need projected or the entire dataset?
$begingroup$
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
1
active
oldest
votes
$begingroup$
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 15 mins ago
Leland McInnesLeland McInnes
1513
$endgroup$
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f43375%2fwhen-projecting-data-with-umap-should-i-use-only-the-samples-i-need-projected-o%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
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 15 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 15 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 15 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 15 mins ago
Leland McInnesLeland McInnes
1513
answered 15 mins ago
Leland McInnesLeland McInnes
1513
answered 15 mins ago
Leland McInnesLeland McInnes
1513
answered 15 mins ago
Leland McInnesLeland McInnes
1513
1513
add a comment |
add a comment |
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f43375%2fwhen-projecting-data-with-umap-should-i-use-only-the-samples-i-need-projected-o%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
$begingroup$
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