Unsupervised Learning

Clustering is a method for finding patterns in data without any prior knowledge of class labels.
The goal is to divide data points into similar groups (clusters).
There are various types of clustering methods mentioned in the sources, including:
- k-Means Clustering:
  - A centroid-based method that attempts to partition the data into a fixed number of clusters, with each cluster represented by its centroid (center).
  - The algorithm minimizes the squared error but can get stuck in a local minimum and is non-deterministic, as its results depend on the random initialization of centroids.
  - The number of clusters (k) is a parameter that needs to be predefined.
- Hierarchical Clustering:
  - A method that builds a tree structure of clusters (dendrogram).
  - There are two main approaches:
    - Top-down (divisive): Begins with one cluster containing all elements and recursively divides it.
    - Bottom-up (agglomerative): Starts with one cluster per element and merges the closest clusters.
  - Various metrics for cluster distances include single linkage (minimum distance), complete linkage (maximum distance), average distance, and centroid distance.
Clustering can be used to identify similar datasets or to organize and simplify data.

Dimension reduction is a technique to reduce the number of variables in a dataset while preserving relevant information.
This technique can be useful for reducing the complexity of data and improving processing efficiency.
An example of dimension reduction is Principal Component Analysis (PCA), which can be considered a non-linear generalization of the autoencoder.

Autoencoders are a type of neural network used for learning data representations.
They consist of an encoder, which transforms the input data into a code (compressed representation), and a decoder, which attempts to reconstruct the input data from this code.
Autoencoders can be used for compression by passing the code through a channel and then having the decoder reconstruct the original data.
After training, the decoder can be discarded, and the code can be used as a data representation.

Erstellt: 14-02-25 15:46

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