[
https://issues.apache.org/jira/browse/LUCENE-9136?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xin-Chun Zhang updated LUCENE-9136:
-----------------------------------
Description:
Representation learning (RL) has been an established discipline in the machine
learning space for decades but it draws tremendous attention lately with the
emergence of deep learning. The central problem of RL is to determine an
optimal representation of the input data. By embedding the data into a high
dimensional vector, the vector retrieval (VR) method is then applied to search
the relevant items.
With the rapid development of RL over the past few years, the technique has
been used extensively in industry from online advertising to computer vision
and speech recognition. There exist many open source implementations of VR
algorithms, such as Facebook's FAISS and Microsoft's SPTAG, providing various
choices for potential users. However, the aforementioned implementations are
all written in C++, and no plan for supporting Java interface
[[https://github.com/facebookresearch/faiss/issues/105]].
The algorithms for vector retrieval can be roughly classified into four
categories,
# Tree-base algorithms, such as KD-tree;
# Hashing methods, such as LSH (Local Sensitive Hashing);
# Product quantization algorithms, such as IVFFlat;
# Graph-base algorithms, such as HNSW, SSG, NSG;
IVFFlat and HNSW are the most popular ones among all the algorithms. Recently,
implementation of ANN algorithms for Lucene, such as HNSW (Hierarchical
Navigable Small World, LUCENE-9004), has made great progress. IVFFlat has
smaller index size but requires k-means clustering, while HNSW is faster in
query but require extra storage for graphs [indexing 1M
vectors|[https://github.com/facebookresearch/faiss/wiki/Indexing-1M-vectors]].
Each of them has its merits and demerits. Since HNSW is now under development,
it may be better to provide IVFFlat for an alternative choice.
I will soon commit my personal implementations.
was:
Representation learning (RL) has been an established discipline in the machine
learning space for decades but it draws tremendous attention lately with the
emergence of deep learning. The central problem of RL is to determine an
optimal representation of the input data. By embedding the data into a high
dimensional vector, the vector retrieval (VR) method is then applied to search
the relevant items.
With the rapid development of RL over the past few years, the technique has
been used extensively in industry from online advertising to computer vision
and speech recognition. There exist many open source implementations of VR
algorithms, such as Facebook's FAISS and Microsoft's SPTAG, providing various
choices for potential users. However, the aforementioned implementations are
all written in C++, and no plan for supporting Java interface
[[https://github.com/facebookresearch/faiss/issues/105]].
The algorithms for vector retrieval can be roughly classified into four
categories,
# Tree-base algorithms, such as KD-tree;
# Hashing methods, such as LSH (Local Sensitive Hashing);
# Product quantization algorithms, such as IVFFlat;
# Graph-base algorithms, such as HNSW, SSG, NSG;
IVFFlat and HNSW are the most popular ones among all the algorithms. Recently,
implementation of ANN algorithms for Lucene, such as HNSW (Hierarchical
Navigable Small World, LUCENE-9004), has made great progress. IVFFlat requires
much less memory and disks when compared with HNSW [indexing 1M
vectors|[https://github.com/facebookresearch/faiss/wiki/Indexing-1M-vectors]].
And IVFFlat supports both online and offline training.
I'm now trying to introduce the IVFFlat to Lucene core in my person branch
[[https://github.com/irvingzhang/lucene-solr/tree/jira/LUCENE-9136]], in very
early stage.
> Introduce IVFFlat for ANN similarity search
> -------------------------------------------
>
> Key: LUCENE-9136
> URL: https://issues.apache.org/jira/browse/LUCENE-9136
> Project: Lucene - Core
> Issue Type: New Feature
> Reporter: Xin-Chun Zhang
> Priority: Major
>
> Representation learning (RL) has been an established discipline in the
> machine learning space for decades but it draws tremendous attention lately
> with the emergence of deep learning. The central problem of RL is to
> determine an optimal representation of the input data. By embedding the data
> into a high dimensional vector, the vector retrieval (VR) method is then
> applied to search the relevant items.
> With the rapid development of RL over the past few years, the technique has
> been used extensively in industry from online advertising to computer vision
> and speech recognition. There exist many open source implementations of VR
> algorithms, such as Facebook's FAISS and Microsoft's SPTAG, providing various
> choices for potential users. However, the aforementioned implementations are
> all written in C++, and no plan for supporting Java interface
> [[https://github.com/facebookresearch/faiss/issues/105]].
> The algorithms for vector retrieval can be roughly classified into four
> categories,
> # Tree-base algorithms, such as KD-tree;
> # Hashing methods, such as LSH (Local Sensitive Hashing);
> # Product quantization algorithms, such as IVFFlat;
> # Graph-base algorithms, such as HNSW, SSG, NSG;
> IVFFlat and HNSW are the most popular ones among all the algorithms.
> Recently, implementation of ANN algorithms for Lucene, such as HNSW
> (Hierarchical Navigable Small World, LUCENE-9004), has made great progress.
> IVFFlat has smaller index size but requires k-means clustering, while HNSW is
> faster in query but require extra storage for graphs [indexing 1M
> vectors|[https://github.com/facebookresearch/faiss/wiki/Indexing-1M-vectors]].
> Each of them has its merits and demerits. Since HNSW is now under
> development, it may be better to provide IVFFlat for an alternative choice.
> I will soon commit my personal implementations.
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