Semanlink - Algorithmes AND Similarity queries / Vector search

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 A propos

GitHub - spotify/annoy: Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

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2018-03-12 A propos

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2017-11-27 A propos

[1004.5370] Self-Taught Hashing for Fast Similarity Search

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2017-11-07 A propos

Finding Similar Items

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**Jaccard similarity**: similarity of sets, based on the relative size of their intersection -> **finding textually similar documents in a large corpus, near duplicates**. [Collaborative Filtering](/tag/collaborative_filtering) as a Similar-Sets Problem (cf. online purchases, movie ratings)

**Shingling** turns the problem of textual similarity of documents into a pb of similarity of sets

k-shingle: substring of length k found within a document. k: 5 for emails. Hashing shingles. Shingles built from words (stop word + 2 following words)

Similarity-Preserving Summaries of Sets: shingles sets are large -> compress large sets into small representations (“signatures”) that preserve similarity: **[Minhashing](/tag/minhash)** - related to Jaccard similarity (good explanation in [wikipedia](https://en.wikipedia.org/wiki/MinHash))

It still may be impossible to find the pairs of docs with greatest similarity efficiently -> **[Locality-Sensitive Hashing](/tag/locality_sensitive_hashing)** for Documents

Distance measures

Theory of Locality-Sensitive Functions

LSH famiies for other distance measures

Applications of Locality-Sensitive Hashing:

- entity resolution
- matching fingerprints
- matching newpapers articles

Methods for High Degrees of Similarity: LSH-based methods most effective when the degree of similarity we
accept is relatively low. When we want to find sets that are almost identical, other methods can be faster.

2017-07-26 A propos