How to use deep learning for data with a feature network structure?pyramid pooling and max pooling in convolution neural networkWhere do filters/kernels for a convolutional network come from?Application of neural network for use with log file dataDeep-learning for mapping large binary inputHow does pre-training improve classification in neural networks?Implementing sparse connections in neural network (Theano)Convolutional neural network and Transfer LearningNeural network architecture for q learningNeural network feature combinatoricsCan I use transfer learning to retrain a Neural Network on different subsets of the data to solve memory problems?
What is the relationship between spectral sequences and obstruction theory?
How do Bards prepare spells?
Is there really no use for MD5 anymore?
How do I use proper grammar in the negation of "have not" for the following sentence translation?
simple conditions equation
French for 'It must be my imagination'?
Does the sign matter for proportionality?
Don’t seats that recline flat defeat the purpose of having seatbelts?
how to sum variables from file in bash
Shrinkwrap tetris shapes without scaling or diagonal shapes
Mac Pro install disk keeps ejecting itself
Examples of non trivial equivalence relations , I mean equivalence relations without the expression " same ... as" in their definition?
Phrase for the opposite of "foolproof"
How to creep the reader out with what seems like a normal person?
Was is really necessary for the Lunar module LM to have 2 stages?
How to type a section sign (§) into the Minecraft client
Realistic Necromancy?
What does KSP mean?
Is the 5 MB static resource size limit 5,242,880 bytes or 5,000,000 bytes?
Error message with tabularx
What route did the Hindenburg take when traveling from Germany to the U.S.?
Controversial area of mathematics
Pass By Reference VS Pass by Value
Was there a shared-world project before "Thieves World"?
How to use deep learning for data with a feature network structure?
pyramid pooling and max pooling in convolution neural networkWhere do filters/kernels for a convolutional network come from?Application of neural network for use with log file dataDeep-learning for mapping large binary inputHow does pre-training improve classification in neural networks?Implementing sparse connections in neural network (Theano)Convolutional neural network and Transfer LearningNeural network architecture for q learningNeural network feature combinatoricsCan I use transfer learning to retrain a Neural Network on different subsets of the data to solve memory problems?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;
I have a general problem in the application domain.The data contains a high dimensional feature space with a small sample.A sparse network with its node as different features are available.The network has edges.The larger the edge, the higher the correlation or dependence the pair of features have.
Generally how I can employ the network information in my model?
Currently I searched in the literature.I find the general approach contains:
1.network embedding.To make use of the network information to obtain an embedding of the features.
2.graph neural network.Like GCN (graph convolutional neural network) or GAT(graph attention neural network) or other message passing neural network.
The question is that what is the general approach a data scientist can have a try, to make use of the network information on the features? The network is not on different samples, just on the features.
graph neural-network deep-learning data-analysis
asked Mar 22 at 18:40
Ferret ZhangFerret Zhang
415
add a comment |
I have a general problem in the application domain.The data contains a high dimensional feature space with a small sample.A sparse network with its node as different features are available.The network has edges.The larger the edge, the higher the correlation or dependence the pair of features have.
Generally how I can employ the network information in my model?
Currently I searched in the literature.I find the general approach contains:
1.network embedding.To make use of the network information to obtain an embedding of the features.
2.graph neural network.Like GCN (graph convolutional neural network) or GAT(graph attention neural network) or other message passing neural network.
The question is that what is the general approach a data scientist can have a try, to make use of the network information on the features? The network is not on different samples, just on the features.
graph neural-network deep-learning data-analysis
asked Mar 22 at 18:40
Ferret ZhangFerret Zhang
415
add a comment |
I have a general problem in the application domain.The data contains a high dimensional feature space with a small sample.A sparse network with its node as different features are available.The network has edges.The larger the edge, the higher the correlation or dependence the pair of features have.
Generally how I can employ the network information in my model?
Currently I searched in the literature.I find the general approach contains:
1.network embedding.To make use of the network information to obtain an embedding of the features.
2.graph neural network.Like GCN (graph convolutional neural network) or GAT(graph attention neural network) or other message passing neural network.
The question is that what is the general approach a data scientist can have a try, to make use of the network information on the features? The network is not on different samples, just on the features.
graph neural-network deep-learning data-analysis
asked Mar 22 at 18:40
Ferret ZhangFerret Zhang
415
I have a general problem in the application domain.The data contains a high dimensional feature space with a small sample.A sparse network with its node as different features are available.The network has edges.The larger the edge, the higher the correlation or dependence the pair of features have.
Generally how I can employ the network information in my model?
Currently I searched in the literature.I find the general approach contains:
1.network embedding.To make use of the network information to obtain an embedding of the features.
2.graph neural network.Like GCN (graph convolutional neural network) or GAT(graph attention neural network) or other message passing neural network.
The question is that what is the general approach a data scientist can have a try, to make use of the network information on the features? The network is not on different samples, just on the features.
graph neural-network deep-learning data-analysis
graph neural-network deep-learning data-analysis
asked Mar 22 at 18:40
Ferret ZhangFerret Zhang
415
asked Mar 22 at 18:40
Ferret ZhangFerret Zhang
415
asked Mar 22 at 18:40
Ferret ZhangFerret Zhang
415
asked Mar 22 at 18:40
Ferret ZhangFerret Zhang
415
asked Mar 22 at 18:40
Ferret ZhangFerret Zhang
415
415
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
The first thing that would come to my mind is to check the feature correlation using the network, and remove highly correlated features before training.
answered Mar 23 at 14:40
Kai AeberliKai Aeberli
662515
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
StackExchange.using("externalEditor", function ()
StackExchange.using("snippets", function ()
StackExchange.snippets.init();
);
);
, "code-snippets");
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "1"
;
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: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
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%2fstackoverflow.com%2fquestions%2f55305954%2fhow-to-use-deep-learning-for-data-with-a-feature-network-structure%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
The first thing that would come to my mind is to check the feature correlation using the network, and remove highly correlated features before training.
answered Mar 23 at 14:40
Kai AeberliKai Aeberli
662515
add a comment |
The first thing that would come to my mind is to check the feature correlation using the network, and remove highly correlated features before training.
answered Mar 23 at 14:40
Kai AeberliKai Aeberli
662515
add a comment |
The first thing that would come to my mind is to check the feature correlation using the network, and remove highly correlated features before training.
answered Mar 23 at 14:40
Kai AeberliKai Aeberli
662515
The first thing that would come to my mind is to check the feature correlation using the network, and remove highly correlated features before training.
answered Mar 23 at 14:40
Kai AeberliKai Aeberli
662515
answered Mar 23 at 14:40
Kai AeberliKai Aeberli
662515
answered Mar 23 at 14:40
Kai AeberliKai Aeberli
662515
answered Mar 23 at 14:40
Kai AeberliKai Aeberli
662515
662515
add a comment |
add a comment |
Thanks for contributing an answer to Stack Overflow!
- 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.
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%2fstackoverflow.com%2fquestions%2f55305954%2fhow-to-use-deep-learning-for-data-with-a-feature-network-structure%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
