A Novel Approach to Regression: Exploring the Similarity Space with Ordinary Least Squares on Database Records

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A Novel Approach to Regression: Exploring the Similarity Space with Ordinary Least Squares on Database Records

Popis výsledku v původním jazyce

The proliferation of textual data, notably in the form of database records, calls for innovative methods of analysis that go beyond traditional numerical techniques. While least squares regression has been a cornerstone in quantitative data analysis, its applicability to textual data remains largely unexplored. This study aims to bridge this gap by introducing a similarity-based least squares method tailored for textual data. Drawing on the principles of similarity measures in text, such as semantic and syntactic closeness, we propose an extension to the conventional least squares framework. Our approach incorporates wordbased similarity metrics into the least squares objective function, enabling the analysis of textual data in a manner coherent with its qualitative nature. The developed methodology is rigorously evaluated using both synthetic and real-world database records, demonstrating its efficacy in uncovering intricate relationships within textual data. Our findings open new avenues for textual data analysis, blending the precision of class.

Název v anglickém jazyce

A Novel Approach to Regression: Exploring the Similarity Space with Ordinary Least Squares on Database Records

Popis výsledku anglicky

The proliferation of textual data, notably in the form of database records, calls for innovative methods of analysis that go beyond traditional numerical techniques. While least squares regression has been a cornerstone in quantitative data analysis, its applicability to textual data remains largely unexplored. This study aims to bridge this gap by introducing a similarity-based least squares method tailored for textual data. Drawing on the principles of similarity measures in text, such as semantic and syntactic closeness, we propose an extension to the conventional least squares framework. Our approach incorporates wordbased similarity metrics into the least squares objective function, enabling the analysis of textual data in a manner coherent with its qualitative nature. The developed methodology is rigorously evaluated using both synthetic and real-world database records, demonstrating its efficacy in uncovering intricate relationships within textual data. Our findings open new avenues for textual data analysis, blending the precision of class.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Conference of Open Innovation Association, FRUCT

ISBN

978-952-65-2460-3

ISSN

2305-7254

e-ISSN

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Počet stran výsledku

8

Strana od-do

270-277

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Riga

Datum konání akce

15. 11. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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