Kohonen Animal Data In Matlab Style

Kohonen Self-Organizing Maps (SOM) for Animal Data Analysis in MATLAB

Self-Organizing Maps (SOMs), also known as Kohonen networks, are powerful unsupervised neural network algorithms used for dimensionality reduction and data visualization. They're particularly useful when dealing with high-dimensional datasets, like those often encountered in animal biology, ecology, and zoology. This article explores the application of Kohonen SOMs to animal data analysis using MATLAB, covering everything from data preparation to visualization and interpretation of the results.

Understanding Kohonen SOMs

A Kohonen SOM is a type of artificial neural network that creates a low-dimensional representation (typically a 2D grid) of a high-dimensional input space. It does this through a process of competitive learning, where neurons compete to "represent" input data points. The neurons are arranged in a grid (e.g., a 2D lattice), and each neuron is associated with a weight vector. During training, the input data is presented to the network, and the neuron whose weight vector is closest (in terms of Euclidean distance or other distance metrics) to the input vector is declared the "winner." The winner and its neighbors then adjust their weight vectors to become more similar to the input vector. This process continues iteratively, causing the network to self-organize into a map where similar data points are clustered together.

Key Components of a Kohonen SOM:

• Input Layer: Receives the high-dimensional input data. The number of input nodes corresponds to the number of features in the dataset.
• Competitive Layer: A grid of neurons (nodes) that compete to be the best match for an input vector.
• Weight Vectors: Each neuron in the competitive layer has a weight vector of the same dimensionality as the input vector. These vectors are adjusted during the training process.
• Neighborhood Function: Defines the extent to which neighboring neurons are updated along with the winning neuron. Common neighborhood functions include Gaussian and rectangular functions.
• Learning Rate: Controls the magnitude of the weight vector adjustments during training. The learning rate typically decreases over time.

Applying Kohonen SOMs to Animal Data in MATLAB

Let's delve into the practical aspects of applying Kohonen SOMs to animal data using MATLAB. We'll use a hypothetical example to illustrate the process. Assume we have a dataset containing various characteristics of different animal species:

1. Data Preparation:

The first step is to prepare the data for use in the SOM. This involves:

• Data Cleaning: Handling missing values (e.g., imputation or removal).
• Data Normalization: Scaling the features to a common range (e.g., 0-1). This is crucial to prevent features with larger values from dominating the training process. MATLAB's mapminmax function is useful for this purpose.
• Feature Selection/Engineering: Selecting the most relevant features or creating new features from existing ones. This step can significantly impact the quality of the SOM results.

% Sample data (replace with your actual data)
data = [180, 120, 80, 15; ...
        200, 110, 60, 18; ...
        6000, 350, 40, 70; ...
        1200, 500, 50, 25; ...
        60, 90, 110, 12; ...
        350, 140, 65, 20];

% Normalize the data
[data_normalized, ps] = mapminmax(data');
data_normalized = data_normalized';

2. Training the Kohonen SOM:

MATLAB's Neural Network Toolbox provides functions for creating and training Kohonen SOMs. The selforgmap function is the primary function for this purpose.

% Define the SOM grid size
grid_size = [5, 5]; % 5x5 grid

% Create the SOM
net = selforgmap(grid_size);

% Train the SOM
net = train(net, data_normalized);

The selforgmap function takes the grid size as input. Experiment with different grid sizes to find the optimal configuration for your data. The train function performs the training process.

3. Visualization and Interpretation:

After training, you can visualize the SOM and interpret the results. Several functions are available for this:

• view(net): Displays the weight vectors of the neurons in the SOM grid. Similar weight vectors are clustered together.
• plotsomtopol: Creates a topographic map that shows the distribution of the input data points across the SOM grid.
• plotsomhits: Creates a histogram showing the number of input vectors that map to each neuron. Neurons with high hit counts represent clusters of similar data points.

% Visualize the SOM
view(net);
plotsomtopol(net, data_normalized);
plotsomhits(net, data_normalized);

By analyzing the visualization, you can identify clusters of animals with similar characteristics. For instance, carnivores might cluster together in one region of the map, while herbivores cluster in another. The topographic map helps to see the distribution of the data points within the map, allowing for a better understanding of how the SOM clusters the data. The hit count shows how frequently each neuron is activated.

4. Advanced Techniques:

• Data Preprocessing: Explore different normalization techniques (e.g., z-score normalization) and feature scaling methods to optimize the SOM performance. Consider Principal Component Analysis (PCA) for dimensionality reduction before applying the SOM.
• SOM Parameters: Experiment with different grid sizes, neighborhood functions, and learning rates to fine-tune the SOM's performance. This often requires iterative experimentation and evaluation.
• Clustering Evaluation Metrics: Quantify the quality of the clustering obtained from the SOM using metrics like Silhouette score or Davies-Bouldin index.
• Data Projection: Use the trained SOM to project new, unseen data points onto the map. This allows for classification and prediction of the characteristics of new data based on the patterns learned by the SOM.

Handling Categorical Variables

The example above used only continuous variables. If your animal data includes categorical variables (e.g., diet, habitat), you need to convert them into a numerical representation before applying the SOM. One common approach is one-hot encoding:

% Example: One-hot encoding for diet
diet = {'Carnivore', 'Carnivore', 'Herbivore', 'Herbivore', 'Carnivore', 'Herbivore'};
diet_encoded = zeros(length(diet), 3); % Assuming 3 diet categories

for i = 1:length(diet)
    if strcmpi(diet{i}, 'Carnivore')
        diet_encoded(i, 1) = 1;
    elseif strcmpi(diet{i}, 'Herbivore')
        diet_encoded(i, 2) = 1;
    % ...add more categories as needed...
    end
end

% Concatenate the encoded categorical variable with the continuous data
data_with_categorical = [data, diet_encoded];

After encoding, you can normalize the data (including the encoded categorical features) and proceed with SOM training as described earlier.

Conclusion

Kohonen SOMs offer a powerful and versatile method for analyzing complex animal datasets. MATLAB's Neural Network Toolbox provides the necessary tools for implementing and visualizing SOMs, enabling researchers to gain valuable insights into the relationships and patterns within their data. Remember to carefully consider data preprocessing, parameter tuning, and visualization techniques to achieve optimal results. The application of advanced techniques like PCA and appropriate clustering evaluation metrics can further enhance the analysis and interpretation of the results. By combining the power of SOMs with careful data analysis and interpretation, researchers can unlock new understandings of animal biology, ecology, and behavior.