Introduction:
Many of the followers of my blog have asked me about LLM, a technique used in machine learning and data mining to solve the problem of nearest neighbor search in high-dimensional spaces. LLM, which stands for «Locality-sensitive Hashing, Local Sensitive Hashing, or Locality Sensitive Learning», is a powerful technique with many applications, including image and video processing, text classification, and recommendation systems. In this article, we will give you a technical overview of LLM, its applications, and where to find more information.

LLM: A Technical Overview of Locality-Sensitive Hashing

LLM is a family of algorithms that map high-dimensional data points to low-dimensional hash codes, such that similar points are mapped to similar codes. This allows for efficient nearest neighbor search, as points that are close together in the high-dimensional space will be mapped to similar codes and can be quickly retrieved. LLM is particularly useful in applications where the dimensionality of the data is high, such as image and video processing, text classification, and recommendation systems.

One good resource for learning more about LLM is the paper «Locality-Sensitive Hashing Scheme Based on p-Stable Distributions», published by researchers Alexandr Andoni and Piotr Indyk in 2008. You can find the paper at this link: https://dl.acm.org/doi/10.1145/1394399.1394401.

Let’s now take a closer look at some applications of LLM:

1. Image and video processing – LLM can be used to quickly retrieve similar images or videos from a large database. This is useful in applications such as image and video search, content recommendation, and facial recognition.

2. Text classification – LLM can be used to classify text documents based on their similarity to other documents. This is useful in applications such as spam filtering, sentiment analysis, and topic modeling.

3. Recommendation systems – LLM can be used to find similar items in a product catalog, which can then be recommended to customers based on their previous purchases or browsing history.

In conclusion, LLM is a powerful technique for solving the problem of nearest neighbor search in high-dimensional spaces. It has many applications in machine learning and data mining, including image and video processing, text classification, and recommendation systems. If you want to learn more about LLM, we recommend checking out the research paper by Andoni and Indyk, and exploring some of the applications we have mentioned.

If you want to learn more about LLM, check out these links:
– «Locality-Sensitive Hashing Scheme Based on p-Stable Distributions» (research paper): https://dl.acm.org/doi/10.1145/1394399.1394401
– «Locality-sensitive hashing» (Wikipedia article): https://en.wikipedia.org/wiki/Locality-sensitive_hashing
– «Locality Sensitive Hashing for Similar Item Search» (tutorial): https://towardsdatascience.com/locality-sensitive-hashing-for-music-search-f2f1940ace23