Unsupervised ANNs Algorithms and Techniques

Unsupervised ANNs Algorithms and Techniques

  • unsupervised ANNs involve
    • Self-organizing maps,
    • Restricted Boltzmann machines,
    • Autoencoders.

Self-organizing maps

  • Self-organizing maps are a basic sort of artificial neural network whose growth depends on unsupervised learning procedures and exploitation of the similarities between data. Self-organizing maps are biologically inspired by topographically computational maps that are learned by self-organization of its neurons. unlike supervised ANN, comprises of input and output neurons with no hidden layers and are developed in such how that just one of the output neurons are often activated.
  • It concludes competitive learning, a procedure where all the output neurons compete with one another . The winner of such competition is fired and mentioned because the winning neuron. With the search of the output neurons having sets of weights which characterize their coordinates within the input space, one method for understanding the competition between output neuron is by computing the worth of the discriminant function, typically Euclidean distance between them and therefore the component vector of the present sample into the input.
  • Self-organizing maps, as their name suggests provide a topographic map that internally portrays the statistical features in input patterns of the supplied input. Initialization, competition, adaptation, and cooperation are the many components involved the self-organization of neurons. At the initial stage, randomly selected small values are initially allocated as weights of output neurons. Output neurons would then compete with one another by comparing the values of the discriminant function.
  • This collaboration is inspired by lateral connections among groups of excited neurons within the human brain.
  • The updated weight received by neighboring neurons may be a function of the lateral distance among them and winning neurons with the closest and farthest neurons accepting the highest and lowest weight update, respectively.
  • The weights are updated for effective unsupervised classification of knowledge. The data behind this is often the necessity to improve the similarity between units that best matches the training input.
  • An undirected graphical model usually mentioned because the best matching unit, and those in a neighborhood to the input. The five phases associated in self-organized maps algorithm are sampling, initialization, finding the neuron whose weight vector best matches the input vector, updating the weights of the winning neuron, and those within the neighborhood using the given equation, and returning the sampling stage until ni(number of inputs) progressions can be implemented within the feature map. Kohonen network may be a kind of self-organized map.
    • ∆wji = ŋTj I(X) (t)(xi- wji)
 Unsupervised ANNs Algorithms and Techniques

Unsupervised ANNs Algorithms and Techniques

  • The Kohonen networks feature map is shown within the figure. Self-organizing maps artificial neural networks are generally applied for clustering and brain-like feature mapping. They're appropriate for application within the areas of exploratory data, statistical, biomedical, financial, industrial, and control analysis.

Restricted Boltzmann Machines

  • Boltzmann machines (BMs) are introduced as bi-directionally connected networks of hypothetical processing units, which may be interpreted as neural network models. Boltzmann machines comprise of two sorts of units, visible and hidden neurons, which may be arranged in two layers.
 Unsupervised ANNs Algorithms and Techniques

markov chain monte carlo

  • Boltzmann machines also can be viewed as specific graphical models, more precisely undirected graphical models, also referred to as Markov random fields. The embedding of BMs into the structure of probabilistic graphical models gives quick access to an abundance of hypothetical outcomes and well-developed algorithms. Computing the probability of an undirected model or its gradient for inference is usually computationally comprehensive. Hence, sampling-based techniques are utilized to estimate the probability and its gradient. Sampling from an undirected graphical model is usually not simple, but form RBMs markov chain monte carlo (MCMC) techniques are easily applicable within the form of Gibbs sampling.
 Unsupervised ANNs Algorithms and Techniques

Unsupervised ANNs Algorithms and Techniques

  • A restricted Boltzmann machine (RBM) may be a Markov random field (MRF) related to a two undirected graph illustrated within the given figure. It includes x visible units V = (V1,…, Vx) to show data and n hidden units H = (H1,…, Hn) to catch observed variables. In binary RBMs, our specialise in the random variables (V, H) takes values (V, H) ∈ {0,1}x+n and therefore the joint probability distribution under the model given by the Gibbs distribution P(V, H) = 1/z e-E(v,h) having the energy function.
 Unsupervised ANNs Algorithms and Techniques

Gibbs Distribution

  • For all i ∈ {1, ..., n} and j ∈ {1, ..., x}, Wij is a real-valued weight connected to the edge between units Vj and Hi, and Bj and Ci are real-valued bias terms connected to the jth visible and the ith hidden variable, respectively.
  • The graph of an RBM has an only association between the layer of hidden and visual variables but not between two variables of an identical layer. In terms of probability, it implies that the hidden variables are independent, given the state of the visible variables and vice versa.


  • An autoencoder may be a quite neural network that's prepared to attempt to repeat its input to its output. Internally, it's a hidden layer that portrays a code used to represent the input.
  • Autoencoder is ANNs with asymmetric structures, where the center layer represents an encoding of the input file. The term autoencoder is that the most popular these days, they were also called auto-associative neural networks, diabolo network, and replicator neural networks.
 Unsupervised ANNs Algorithms and Techniques


  • The basic structure of an autoencoder is shown within the figure given below. It incorporates an input p that's mapped onto the encoding b through an encoder, represented as function F. This encoding is mapped to be a recreation r utilizing a decoder, represented as function Z.
  • This structure is captured during a feedforward neural network. Since the goal is to reproduce the input data on the output layer, both p and r have an identical dimension. p can have higher-dimension or lower dimensions, depending upon the desired properties. The autoencoder also can have various layers as needed, generally placed symmetrically within the encoder and decoder. Such neural architecture is often seen within the figure given below.
 Unsupervised ANNs Algorithms and Techniques

Structure od Auto Encoder

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