Autoencoder model and its applications
An autoencoder is a type of neural network that transfers input value to output value, as shown in figure (1). Since the autoencoder model does not need …
An autoencoder is a type of neural network that transfers input value to output value, as shown in figure (1). Since the autoencoder model does not need a target value (Y), we can call it unsupervised learning.
In the autoencoder model, we are not interested in the output layer. In fact, in this model, hidden core layers are essential. If hidden layers are less than input layers, the hidden layers will extract the necessary information of the input values.
The hidden layers learn the main patterns of data and remove the noises. In the autoencoder model, the hidden layers have fewer dimensions than the input and output layers. If the number of neurons in hidden layers is more than layers in input layers, the neural network will be given too much capacity to learn the data. Sometimes, it may copy the input to the output values, including noises.
Figure (1) also shows the encoding and decoding process. The encoding process compacts the input values to get core layers. The decoding layers rebuild the information to produce the output. The encoding layers mirror the encoding layers in the number of hidden layers and neurons.
Applications of autoencoder model
1 - The early application of autoencoder is dimensionality reduction.
2 - The autoencoder models also have broad applications in computer vision and image editing.
3 - In image coloring, autoencoders convert a black-and-white image to a colored one.
4 – The autoencoder models are used to remove noises.
_In many distance-based techniques like KNN, high dimensionality is the main problem and must be reduced. In the autoencoder models, outliers are identified and will not be removed during dimensionality reduction._
There are many tools to detect outliers, like PCA. But PCA uses linear algebra to transform. In contrast, the autoencoder models can perform non-linear transformations with their non-linear activation function and multiplier layers. Training several layers with an autoencoder is more efficient than training one huge transformation with PCA. The autoencoder technique is the best option when the data is complex and non-linear.