Answers.Body: <p>With this link I could read the paper. Thanks.</p>
  <p>So there is this discipline called sensor fusion. It is very sounded in the field of Autonomous Vehicles where in order to take one decision (whether to break or not) you have to take into account information for multiple sources: car mounted cameras, LIDAR, ultrasound, radar...</p>
  <p>So the term &quot;fusion&quot; refers to the operation of aggregating the information from multiple sources (that has its problems as the paper says: <em>the signals measured by multiple sensors are disordered and correlated with multiple sources</em>). In order to perform this fusion or aggregation you can aggregate the information in different levels of the processing pipeline: close to raw data (signal fusion), close to high level information (decision fusion) or something in the middle (feature fusion).</p>
  <p>Normally when you have a signal (image, radar, electromagenic...) you proces it somehow (normally using filters). The output of those filters is a feature map (in case of 2D images) or feature vectors (in case of 1D signals). Usually when you have those signals processed you use a decision module to extract high level information (a classification head, a SVM, a regressor...).</p>
  <p><a href="https://i.sstatic.net/9X4mL.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/9X4mL.png" alt="enter image description here" /></a></p>
  <p>Basically you can aggregate information at those 3 levels. The authors refer as <em>feature fusion</em> as to aggregate information in the middle step. You do not have raw data, but you do not have refined data either. They do so expecting they remove the noisy part of the signals (filters) or the non relevant parts (PCA) but without removing the nuances lost when using the decision modules.</p>
  <p>The name <em>&quot;feature fusion&quot;</em> comes from the deep learning terminology in which when you have a signal and you process it somehow the output of it is a feature. When reading a paper on image detection / classification you would see <em>&quot;feature maps&quot;</em> but when reading a paper on sensor or audio you would read <em>&quot;feature vector&quot;</em> hence then name</p>
  
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Answers.CreationDate: 2021-07-28T12:13:29.740Z
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Answers.UserDisplayName: JVGD
Body: <p>I encountered the phrase &quot;fusing features&quot; several times in the literature. I am providing an excerpt from <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795921/" rel="nofollow noreferrer">a research paper</a> to provide context for usage of the word <strong>fusion</strong>.</p>
  <blockquote>
  <p>The reason is that the signals measured by multiple sensors are
  disordered and correlated with multiple sources. <strong>Those methods that
  are proposed with an attempt to use multiple data sources are called
  data fusion techniques</strong>. Upon the position where the <strong>fusion
  operation</strong> is conducted, there are three general approaches:
  signal-level fusion, feature-level fusion, and decision-level fusion.</p>
  </blockquote>
  <p>I am guessing that &quot;fusing features&quot; refers to an act of combining several features, from different domains, and then generating new features that serves the purpose of fusing.</p>
  <p>If yes, the word &quot;fusion&quot; here refers to its <a href="https://www.lexico.com/definition/fusion" rel="nofollow noreferrer">common English usage</a></p>
  <blockquote>
  <p>The process or result of joining two or more things together to form a
  single entity.</p>
  </blockquote>
  <p>That is,we need to combine multiple features in <strong>any</strong> manner and then coming up with new features that are good enough to perform our AI task.</p>
  <p>Or does it have any formal definition and requirements based on the input or output features? Is there any formal definition for fusion operator?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-28T10:53:59.280Z
Comments.Text: I could not read the paper since it is not public, if you hand a copy I will answer, I am pretty sure what they mean, but I would have to take a look at the paper
Comments.UserDisplayName: JVGD
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-28T11:08:43.360Z
Comments.Text: @JVGD Can you access now?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-29T07:21:21.353Z
Comments.Text: Yes @hanugm I finally could!
Comments.UserDisplayName: JVGD
Tags: <terminology><definitions>
Title: Does "fusion" in "feature fusion" has any formal definition?
UserDisplayName: hanugm
ThreadText: # Does "fusion" in "feature fusion" has any formal definition?
  
  (Asked by: username_0 on 2021-07-28)
  
  I encountered the phrase "fusing features" several times in the literature. I am providing an excerpt from [a research paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795921/) to provide context for usage of the word **fusion**.
  
  > The reason is that the signals measured by multiple sensors are
  > disordered and correlated with multiple sources. **Those methods that
  > are proposed with an attempt to use multiple data sources are called
  > data fusion techniques**. Upon the position where the **fusion
  > operation** is conducted, there are three general approaches:
  > signal-level fusion, feature-level fusion, and decision-level fusion.
  
  I am guessing that "fusing features" refers to an act of combining several features, from different domains, and then generating new features that serves the purpose of fusing.
  
  If yes, the word "fusion" here refers to its [common English usage](https://www.lexico.com/definition/fusion)
  
  > The process or result of joining two or more things together to form a
  > single entity.
  
  That is,we need to combine multiple features in **any** manner and then coming up with new features that are good enough to perform our AI task.
  
  Or does it have any formal definition and requirements based on the input or output features? Is there any formal definition for fusion operator?
  
  **Tags:** terminology, definitions
  
  > **Comments:**
  > **username_1**: I could not read the paper since it is not public, if you hand a copy I will answer, I am pretty sure what they mean, but I would have to take a look at the paper  
  > **username_0**: @username_1 Can you access now?  
  > **username_1**: Yes @username_0 I finally could!  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-07-28. Score: 2
  
  With this link I could read the paper. Thanks.
  
  So there is this discipline called sensor fusion. It is very sounded in the field of Autonomous Vehicles where in order to take one decision (whether to break or not) you have to take into account information for multiple sources: car mounted cameras, LIDAR, ultrasound, radar...
  
  So the term "fusion" refers to the operation of aggregating the information from multiple sources (that has its problems as the paper says: *the signals measured by multiple sensors are disordered and correlated with multiple sources*). In order to perform this fusion or aggregation you can aggregate the information in different levels of the processing pipeline: close to raw data (signal fusion), close to high level information (decision fusion) or something in the middle (feature fusion).
  
  Normally when you have a signal (image, radar, electromagenic...) you proces it somehow (normally using filters). The output of those filters is a feature map (in case of 2D images) or feature vectors (in case of 1D signals). Usually when you have those signals processed you use a decision module to extract high level information (a classification head, a SVM, a regressor...).
  
  [![enter image description here](https://i.sstatic.net/9X4mL.png)](https://i.sstatic.net/9X4mL.png)
  
  Basically you can aggregate information at those 3 levels. The authors refer as *feature fusion* as to aggregate information in the middle step. You do not have raw data, but you do not have refined data either. They do so expecting they remove the noisy part of the signals (filters) or the non relevant parts (PCA) but without removing the nuances lost when using the decision modules.
  
  The name *"feature fusion"* comes from the deep learning terminology in which when you have a signal and you process it somehow the output of it is a feature. When reading a paper on image detection / classification you would see *"feature maps"* but when reading a paper on sensor or audio you would read *"feature vector"* hence then name
  
  
  ---
  
  