Deep Clustering for Financial Market Segmentation

Unsupervised learning, supervised learning and reinforcement learning are three main categories of machine learning methods. Unsupervised learning has many applications such as clustering, dimensionality reduction, etc. The machine learning algorithms K-means and Principal Component Analysis (PCA) are widely used for clustering and dimensionality reduction respectively. Similarly to PCA, the T-distributed Stochastic Neighbor Embedding (t-SNE) is another unsupervised machine learning algorithm for dimensionality reduction. With the advancement of unsupervised deep learning, the Autoencoder neural network is now frequently used for high dimensionality (e.g., a dataset with thousands or more features) reduction. Autoencoder can also be combined with supervised learning (e.g., Random Forest) to form Semi-supervised learning method (see deep patient as an example).