Data-driven Learned Metric Index: an Unsupervised Approach

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00119191" target="_blank" >RIV/00216224:14330/21:00119191 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-89657-7_7" target="_blank" >http://dx.doi.org/10.1007/978-3-030-89657-7_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-89657-7_7" target="_blank" >10.1007/978-3-030-89657-7_7</a>

Result language
angličtina
Original language name
Data-driven Learned Metric Index: an Unsupervised Approach
Original language description
Metric indexes are traditionally used for organizing unstructured or complex data to speed up similarity queries. The most widely-used indexes cluster data or divide space using hyper-planes. While searching, the mutual distances between objects and the metric properties allow for the pruning of branches with irrelevant data -- this is usually implemented by utilizing selected anchor objects called pivots. Recently, we have introduced an alternative to this approach called Lear-ned Metric Index. In this method, a series of machine learning models substitute decisions performed on pivots -- the query evaluation is then determined by the predictions of these models. This technique relies upon a traditional metric index as a template for its own structure -- this dependence on a pre-existing index and the related overhead is the main drawback of the approach. In this paper, we propose a data-driven variant of the Learned Metric Index, which organizes the data using their descriptors directly, thus eliminating the need for a template. The proposed learned index shows significant gains in performance over its earlier version, as well as the established indexing structure M-index.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA19-02033S" target="_blank" >GA19-02033S: Searching, Mining, and Annotating Human Motion Streams</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
14th International Conference on Similarity Search and Applications (SISAP 2021)
ISBN
9783030896560
ISSN
0302-9743
e-ISSN
—
Number of pages
14
Pages from-to
81-94
Publisher name
Springer
Place of publication
Cham
Event location
Dortmund, Germany
Event date
Jan 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000722252200007

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