High level vector quantization operators.
Growing Neural Gas.
Growing Neural Gas. As an extension of Neural Gas, Growing Neural Gas can add and delete nodes during algorithm execution. The growth mechanism is based on growing cell structures and competitive Hebbian learning.
Compared to Neural Gas, GNG has the following distinctions:
the data set.
the number of epochs of learning.
the fraction to update nearest neuron.
the fraction to update neighbors of nearest neuron.
the maximum age of edges.
if the number of input signals so far is an integer multiple of lambda, insert a new neuron.
decrease error variables by multiplying them with alpha during inserting a new neuron.
decrease all error variables by multiply them with beta.
Neural Gas soft competitive learning algorithm.
Neural Gas soft competitive learning algorithm. The Neural Gas is inspired by the Self-Organizing Map for finding optimal data representations based on feature vectors. The algorithm was coined "Neural Gas" because of the dynamics of the feature vectors during the adaptation process, which distribute themselves like a gas within the data space. Although it is mainly applied where data compression or vector quantization is an issue, it is also used for cluster analysis as a robustly converging alternative to the k-means clustering. A prominent extension is the Growing Neural Gas.
Compared to SOM, neural gas has no topology of a fixed dimensionality (in fact, no topology at all). For each input signal during learning, the neural gas algorithm sorts the neurons of the network according to the distance of their reference vectors to the input signal. Based on this "rank order", neurons are adapted based on the adaptation strength that are decreased according to a fixed schedule.
The adaptation step of the Neural Gas can be interpreted as gradient descent on a cost function. By adapting not only the closest feature vector but all of them with a step size decreasing with increasing distance order, compared to k-means clustering, a much more robust convergence of the algorithm can be achieved.
the data set.
the number of units in the neural gas.
the initial value of lambda. lambda_i and lambda_f are used to set the soft learning radius/rate, i.e. determining the number of neural units significantly changing their synaptic weights with each adaptation step.
The final value of lambda.
the initial value of epsilon. epsilon_i and epsilon_f are the initial and final learning rate respectively.
the final value of epsilon.
the number of iterations. Note that for one iteration, we mean that the learning process goes through the whole dataset.
NeuralMap is an efficient competitive learning algorithm inspired by growing neural gas and BIRCH.
NeuralMap is an efficient competitive learning algorithm inspired by growing neural gas and BIRCH. Like growing neural gas, NeuralMap has the ability to add and delete neurons with competitive Hebbian learning. Edges exist between neurons close to each other. The edges are intended place holders for localized data distribution. The edges also help to locate distinct clusters (those clusters are not connected by edges). NeuralMap employs Locality-Sensitive Hashing to speedup the learning while BIRCH uses balanced CF trees.
the data set.
the distance radius to activate a neuron for a given signal.
the number of hash tables.
the number of random projection hash functions.
the fraction to update activated neuron.
the fraction to update neighbors of activated neuron.
Self-Organizing Map.
Self-Organizing Map. An SOM is a unsupervised learning method to produce a low-dimensional (typically two-dimensional) discretized representation (called a map) of the input space of the training samples. The model was first described as an artificial neural network by Teuvo Kohonen, and is sometimes called a Kohonen map.
While it is typical to consider SOMs as related to feed-forward networks where the nodes are visualized as being attached, this type of architecture is fundamentally different in arrangement and motivation because SOMs use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling.
SOMs belong to a large family of competitive learning process and vector quantization. An SOM consists of components called nodes or neurons. Associated with each node is a weight vector of the same dimension as the input data vectors and a position in the map space. The usual arrangement of nodes is a regular spacing in a hexagonal or rectangular grid. The self-organizing map describes a mapping from a higher dimensional input space to a lower dimensional map space. During the (iterative) learning, the input vectors are compared to the weight vector of each neuron. Neurons who most closely match the input are known as the best match unit (BMU) of the system. The weight vector of the BMU and those of nearby neurons are adjusted to be closer to the input vector by a certain step size.
There are two ways to interpret a SOM. Because in the training phase weights of the whole neighborhood are moved in the same direction, similar items tend to excite adjacent neurons. Therefore, SOM forms a semantic map where similar samples are mapped close together and dissimilar apart. The other way is to think of neuronal weights as pointers to the input space. They form a discrete approximation of the distribution of training samples. More neurons point to regions with high training sample concentration and fewer where the samples are scarce.
SOM may be considered a nonlinear generalization of Principal components analysis (PCA). It has been shown, using both artificial and real geophysical data, that SOM has many advantages over the conventional feature extraction methods such as Empirical Orthogonal Functions (EOF) or PCA.
It has been shown that while SOMs with a small number of nodes behave in a way that is similar to K-means. However, larger SOMs rearrange data in a way that is fundamentally topological in character and display properties which are emergent. Therefore, large maps are preferable to smaller ones. In maps consisting of thousands of nodes, it is possible to perform cluster operations on the map itself.
A common way to display SOMs is the heat map of U-matrix. The U-matrix value of a particular node is the minimum/maximum/average distance between the node and its closest neighbors. In a rectangular grid for instance, we might consider the closest 4 or 8 nodes.
the dataset for clustering.
the width of map.
the height of map.
Originally used for data compression, Vector quantization (VQ) allows the modeling of probability density functions by the distribution of prototype vectors. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in K-Means and some other clustering algorithms.
Vector quantization is is based on the competitive learning paradigm, and also closely related to sparse coding models used in deep learning algorithms such as autoencoder.
Algorithms in this package also support the
partition
method for clustering purpose.