Hashing

See also: Machine learning terms

Hashing in Machine Learning

Hashing, a technique commonly used in computer science, has found various applications in the field of machine learning. In this context, hashing mainly refers to the process of converting high-dimensional input data into lower-dimensional representations, while preserving important information about the original data. This transformation can be beneficial for numerous machine learning tasks, including feature extraction, dimensionality reduction, and efficient storage and retrieval of data.

Feature Hashing

Feature hashing, also known as the hashing trick, is a technique employed in machine learning to efficiently represent high-dimensional feature vectors in a lower-dimensional space. It involves using a hash function to map features to a fixed-length vector of integers. The primary advantage of feature hashing is its ability to handle large and sparse data, as well as its scalability and computational efficiency.

One common application of feature hashing is in natural language processing, particularly for text classification tasks. In these cases, raw text data is transformed into numerical representations that can be fed into machine learning algorithms. The hashing trick allows for a more efficient representation of text data, reducing the computational burden and memory requirements of the model.

Locality Sensitive Hashing (LSH)

Locality Sensitive Hashing (LSH) is a hashing-based technique designed to identify similar items within a dataset. LSH computes hash codes for the data points, such that similar items have a higher probability of being assigned to the same hash bucket. This property enables efficient nearest neighbor search and approximate similarity computation in high-dimensional spaces.

Approximate Nearest Neighbor (ANN) search is a common application of LSH in machine learning, particularly for tasks like image retrieval, document clustering, and recommendation systems. By leveraging LSH, these tasks can be performed more efficiently and with lower computational overhead compared to exact nearest neighbor search.

Explain Like I'm 5 (ELI5)

Imagine you have a huge box of differently-shaped and colored LEGO bricks. Now, you want to group them based on their color and shape to make it easier to find the one you need. Hashing in machine learning is like organizing these LEGO bricks into smaller containers with labels, so you can easily find the one you're looking for.

In machine learning, we deal with lots of data, like words in a book or images on the internet. Hashing helps us organize this data in a simpler and more efficient way, so computers can understand and work with it faster. It's like putting the LEGO bricks into labeled containers, so you can find them more easily when you want to build something.