NMF (Non-negative Matrix Factorization)

Step 1: Preprocess the text data by tokenizing and removing stop words.

Step 2: Create a term-document matrix, where rows represent terms and columns represent documents.

Step 3: Apply NMF to the term-document matrix to extract topic vectors.

Step 4: Assign each document to the topic with the highest weight, creating document clusters.

Step 1: Represent each image as a matrix, where rows and columns correspond to pixels.

Step 2: Apply NMF to the image matrix to extract basis images or features.

Step 3: Use the extracted features as input to a classifier or other machine learning model.

Step 4: Evaluate the performance of the model on a held-out test set.

Step 1: Create a user-item interaction matrix, where rows represent users and columns represent items.

Step 2: Apply NMF to the interaction matrix to extract latent user and item features.

Step 3: Predict user preferences for unrated items based on the dot product of their latent user and item features.

Step 4: Recommend items with the highest predicted preference scores to the user.

Step 1: Represent gene expression data as a matrix, where rows represent genes and columns represent samples.

Step 2: Apply NMF to the gene expression matrix to extract gene expression signatures.

Step 3: Identify genes that are highly correlated with each signature.

Step 4: Use the signatures to classify samples into different subtypes or conditions.