What Sparsity Meaning, Applications & Example
Measure of how many zero values exist in a dataset or model.
What is Sparsity?
Sparsity refers to the condition where most of the elements in a dataset, matrix, or vector are zero or absent. In machine learning and data science , sparsity is common in high-dimensional data, where many features or attributes are not present or have zero values. Sparsity is often used to reduce the computational cost of algorithms by focusing on the non-zero values, leading to more efficient storage and processing.
Types of Sparsity
- Matrix Sparsity: In a sparse matrix, most of the elements are zero. Techniques like sparse matrix storage formats (e.g., Compressed Sparse Row (CSR)) are used to store and compute only non-zero elements.
- Feature Sparsity: In high-dimensional datasets, many features may have no meaningful value for a given instance, leading to sparse feature vectors.
Applications of Sparsity
- Recommendation Systems: Sparsity is common in user-item interaction matrices, where most users interact with only a small subset of items.
- Natural Language Processing (NLP) : Text data, represented as word vectors, often leads to sparse matrices where most words are absent in any given document.
- Image Compression: Sparsity is leveraged to store only the non-zero coefficients in compressed image formats.
Example of Sparsity
An example of sparsity can be found in a movie recommendation system where most users have rated only a small fraction of the available movies. Instead of storing ratings for all movies, the system can focus on the non-zero ratings, which are significantly fewer, to improve storage efficiency and processing speed.