The LVQ network architecture is shown below.

An LVQ network has a first competitive layer and a second linear layer. The
competitive layer learns to classify input vectors in much the same way as the
competitive layers of Cluster with Self-Organizing Map Neural Network described in this topic. The linear layer
transforms the competitive layer’s classes into target classifications defined by
the user. The classes learned by the competitive layer are referred to as *subclasses* and the classes of the linear layer as *target classes*.

Both the competitive and linear layers have one neuron per (sub or target) class.
Thus, the competitive layer can learn up to
*S*^{1} subclasses. These, in turn, are
combined by the linear layer to form
*S*^{2} target classes.
(*S*^{1} is always larger than
*S*^{2}.)

For example, suppose neurons 1, 2, and 3 in the competitive layer all learn
subclasses of the input space that belongs to the linear layer target class 2. Then
competitive neurons 1, 2, and 3 will have **LW**^{2,1 }weights of 1.0 to neuron **n**^{2} in the linear layer, and
weights of 0 to all other linear neurons. Thus, the linear neuron produces a 1 if
any of the three competitive neurons (1, 2, or 3) wins the competition and outputs a
1. This is how the subclasses of the competitive layer are combined into target
classes in the linear layer.

In short, a 1 in the *i*th row of **a**^{1} (the rest to the elements of **a**^{1} will be zero) effectively
picks the *i*th column of **LW**^{2,1} as the network output. Each such
column contains a single 1, corresponding to a specific class. Thus, subclass 1s
from layer 1 are put into various classes by the **LW**^{2,1}**a**^{1} multiplication in layer 2.

You know ahead of time what fraction of the layer 1 neurons should be classified
into the various class outputs of layer 2, so you can specify the elements of
**LW**^{2,1} at the start.
However, you have to go through a training procedure to get the first layer to
produce the correct subclass output for each vector of the training set. This
training is discussed in Training.
First, consider how to create the original network.

You can create an LVQ network with the function `lvqnet`

,

net = lvqnet(S1,LR,LF)

where

`S1`

is the number of first-layer hidden neurons.`LR`

is the learning rate (default 0.01).`LF`

is the learning function (default is`learnlv1`

).

Suppose you have 10 input vectors. Create a network that assigns each of these input vectors to one of four subclasses. Thus, there are four neurons in the first competitive layer. These subclasses are then assigned to one of two output classes by the two neurons in layer 2. The input vectors and targets are specified by

P = [-3 -2 -2 0 0 0 0 2 2 3; 0 1 -1 2 1 -1 -2 1 -1 0];

and

Tc = [1 1 1 2 2 2 2 1 1 1];

It might help to show the details of what you get from these two lines of code.

P,Tc P = -3 -2 -2 0 0 0 0 2 2 3 0 1 -1 2 1 -1 -2 1 -1 0 Tc = 1 1 1 2 2 2 2 1 1 1

A plot of the input vectors follows.

As you can see, there are four subclasses of input vectors. You want a network
that classifies **p**_{1},
**p**_{2}, **p**_{3}, **p**_{8}, **p**_{9}, and **p**_{10 }to produce an output of 1, and that
classifies vectors **p**_{4},
**p**_{5}, **p**_{6}, and **p**_{7} to produce an output of 2. Note that this
problem is nonlinearly separable, and so cannot be solved by a perceptron, but an
LVQ network has no difficulty.

Next convert the `Tc`

matrix to target vectors.

T = ind2vec(Tc);

This gives a sparse matrix `T`

that can be displayed in full
with

targets = full(T)

which gives

targets = 1 1 1 0 0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0

This looks right. It says, for instance, that if you have the first column of
`P`

as input, you should get the first column of
`targets`

as an output; and that output says the input falls in
class 1, which is correct. Now you are ready to call
`lvqnet`

.

Call `lvqnet`

to create a network with four neurons.

net = lvqnet(4);

Configure and confirm the initial values of the first-layer weight matrix are initialized by the function midpoint to values in the center of the input data range.

net = configure(net,P,T); net.IW{1} ans = 0 0 0 0 0 0 0 0

Confirm that the second-layer weights have 60% (6 of the 10 in
`Tc`

) of its columns with a 1 in the first row, (corresponding
to class 1), and 40% of its columns have a 1 in the second row (corresponding to
class 2). With only four columns, the 60% and 40% actually round to 50% and there
are two 1's in each row.

net.LW{2,1} ans = 1 1 0 0 0 0 1 1

This makes sense too. It says that if the competitive layer produces a 1 as the first or second element, the input vector is classified as class 1; otherwise it is a class 2.

You might notice that the first two competitive neurons are connected to the first linear neuron (with weights of 1), while the second two competitive neurons are connected to the second linear neuron. All other weights between the competitive neurons and linear neurons have values of 0. Thus, each of the two target classes (the linear neurons) is, in fact, the union of two subclasses (the competitive neurons).

You can simulate the network with `sim`

. Use the original `P`

matrix as input just to
see what you get.

Y = net(P); Yc = vec2ind(Y) Yc = 1 1 1 1 1 1 1 1 1 1

The network classifies all inputs into class 1. Because this is not what you want, you have to train the network (adjusting the weights of layer 1 only), before you can expect a good result. The next two sections discuss two LVQ learning rules and the training process.

LVQ learning in the competitive layer is based on a set of input/target pairs.

$$\left\{{p}_{1},{t}_{1}\right\},\left\{{p}_{2},{t}_{2}\right\},\dots \left\{{p}_{Q},{t}_{Q}\right\}$$

Each target vector has a single 1. The rest of its elements are 0. The 1 tells the proper classification of the associated input. For instance, consider the following training pair.

$$\left\{{p}_{1}=\left[\begin{array}{c}2\\ -1\\ 0\end{array}\right],{t}_{1}=\left[\begin{array}{l}0\\ 0\\ 1\\ 0\end{array}\right]\right\}$$

Here there are input vectors of three elements, and each input vector is to be assigned to one of four classes. The network is to be trained so that it classifies the input vector shown above into the third of four classes.

To train the network, an input vector **p** is
presented, and the distance from **p** to each row of
the input weight matrix **IW**^{1,1} is computed with the function
`negdist`

. The hidden neurons of layer
1 compete. Suppose that the *i*th element of **n**^{1} is most positive, and neuron
*i** wins the competition. Then the competitive transfer
function produces a 1 as the *i**th element of **a**^{1}. All other elements of
**a**^{1} are 0.

When **a**^{1} is multiplied
by the layer 2 weights **LW**^{2,1}, the single 1 in **a**^{1} selects the class
*k** associated with the input. Thus, the network has assigned
the input vector **p** to class *k**
and α^{2}_{k*} will be 1. Of course,
this assignment can be a good one or a bad one, for
*t _{k*}* can be 1 or 0, depending on
whether the input belonged to class

Adjust the *i**th row of **IW**^{1,1} in such a way as to move this row
closer to the input vector **p** if the assignment is
correct, and to move the row away from **p** if the
assignment is incorrect. If **p** is classified
correctly,

$$\left({\alpha}_{k\ast}^{2}={t}_{k\ast}=1\right)$$

compute the new value of the *i**th row of **IW**^{1,1} as

$${}_{i\ast}I{W}^{1,1}(q)={}_{i\ast}I{W}^{1,1}(q-1)+\alpha (p(q)-{}_{i\ast}I{W}^{1,1}(q-1))$$

On the other hand, if **p** is classified
incorrectly,

$$\left({\alpha}_{k\ast}^{2}=1\ne {t}_{k\ast}=0\right)$$

compute the new value of the *i**th row of **IW**^{1,1} as

$${}_{i\ast}I{W}^{1,1}(q)={}_{i\ast}I{W}^{1,1}(q-1)-\alpha (p(q)-{}_{i\ast}I{W}^{1,1}(q-1))$$

You can make these corrections to the *i**th row of **IW**^{1,1} automatically, without
affecting other rows of **IW**^{1,1}, by back-propagating the output
errors to layer 1.

Such corrections move the hidden neuron toward vectors that fall into the class for which it forms a subclass, and away from vectors that fall into other classes.

The learning function that implements these changes in the layer 1 weights in LVQ
networks is `learnlv1`

. It can be applied during
training.

Next you need to train the network to obtain first-layer weights that lead to the
correct classification of input vectors. You do this with `train`

as with the following commands. First, set the training epochs
to 150. Then, use `train`

:

net.trainParam.epochs = 150; net = train(net,P,T);

Now confirm the first-layer weights.

net.IW{1,1} ans = 0.3283 0.0051 -0.1366 0.0001 -0.0263 0.2234 0 -0.0685

The following plot shows that these weights have moved toward their respective classification groups.

To confirm that these weights do indeed lead to the correct classification, take
the matrix `P`

as input and simulate the network. Then see what
classifications are produced by the network.

Y = net(P); Yc = vec2ind(Y)

This gives

Yc = 1 1 1 2 2 2 2 1 1 1

which is expected. As a last check, try an input close to a vector that was used in training.

pchk1 = [0; 0.5]; Y = net(pchk1); Yc1 = vec2ind(Y)

This gives

Yc1 = 2

This looks right, because `pchk1`

is close to other vectors
classified as 2. Similarly,

pchk2 = [1; 0]; Y = net(pchk2); Yc2 = vec2ind(Y)

gives

Yc2 = 1

This looks right too, because `pchk2`

is close to other vectors
classified as 1.

You might want to try the example program Learning Vector Quantization. It follows the discussion of training given above.

The following learning rule is one that might be applied
*after* first applying LVQ1. It can improve the result of the
first learning. This particular version of LVQ2 (referred to as LVQ2.1 in the
literature [Koho97]) is embodied in
the function `learnlv2`

. Note again that LVQ2.1 is
to be used only after LVQ1 has been applied.

Learning here is similar to that in `learnlv2`

except now two vectors of layer 1 that are closest to the
input vector can be updated, provided that one belongs to the correct class and one
belongs to a wrong class, and further provided that the input falls into a
“window” near the midplane of the two vectors.

The window is defined by

$$\mathrm{min}\left(\frac{{d}_{i}}{{d}_{j}},\frac{{d}_{j}}{{d}_{i}}\right)>s$$

where

$$s\equiv \frac{1-w}{1+w}$$

(where *d _{i}* and

The adjustments made are

$${}_{i\ast}I{W}^{1,1}(q)={}_{i\ast}I{W}^{1,1}(q-1)-\alpha (p(q)-{}_{i\ast}I{W}^{1,1}(q-1))$$

and

$${}_{j\ast}I{W}^{1,1}(q)={}_{j\ast}I{W}^{1,1}(q-1)+\alpha (p(q)-{}_{j\ast}I{W}^{1,1}(q-1))$$

Thus, given two vectors closest to the input, as long as one belongs to the wrong class and the other to the correct class, and as long as the input falls in a midplane window, the two vectors are adjusted. Such a procedure allows a vector that is just barely classified correctly with LVQ1 to be moved even closer to the input, so the results are more robust.

Function | Description |
---|---|

Create a competitive layer. | |

Kohonen learning rule. | |

Create a self-organizing map. | |

Conscience bias learning function. | |

Distance between two position vectors. | |

Euclidean distance weight function. | |

Link distance function. | |

Manhattan distance weight function. | |

Gridtop layer topology function. | |

Hexagonal layer topology function. | |

Random layer topology function. | |

Create a learning vector quantization network. | |

LVQ1 weight learning function. | |

LVQ2 weight learning function. |