The term feature embedding appears to be a synonym for feature extraction, feature learning etc. I.e. a form of embedding/dimension reduction (with the caveat the goal may not be a lower dimensional representation but one of equal dimensionality, but more meaningfully expressed):
Feature embedding is an emerging research area which intends to transform ...
Dimensionality reduction could be achieved by using an Autoencoder Network, which learns a representation (or Encoding) for the input data. While training, the reduction side (Encoder) reduces the data to a lower-dimension and a reconstructing side (Decoder) tries to reconstruct the original input from the intermediate reduced encoding.
You could assign ...
It can definitely be learned, the question is the approach. It would be expensive and difficult from a modeling directive to do this Densely, so usually convolutions are the way to go. An issue with convolutions is that is generally focuses on equivariant and relative features, so if you need specific location within the approach might be worth the simple ...
Some examples of dimensionality reduction techniques:
Graph-based methods ("Network embedding")
Graph-based kernel PCA
Though there are many more.
manual feature engineering started becoming obsolete
That is wrong.
Any suggestion on when to use manual feature engineering, feature learning or a combination of the two?
Deep learning is awesome for natural signals like images, audio or large amounts of unstructured text (e.g. arbitrary crawled websites)
There are some basic steps that make almost ...
Feature embeddings are basically anything that can act as a hidden representation for given object.
In the case of images, a CNN architecture is built to create such hidden representation. Usually, the outcome of the bottleneck layer is flattened (and sometimes, converted to even lower dimensional space by adding one more dense layer) and used as feature ...