ANALYSIS OF RELATIONSHIPS BETWEEN TRAINING SAMPLE OBJECTS ON MANIFOLDS OF LATENT FEATURE SETS
DOI:
https://doi.org/10.65164/43sjv566Kalit so‘zlar:
latent features, dimensionality reduction, hierarchical agglomerative clustering, pattern recognition, compactness measure.Abstrak
This paper proposes an approach to pattern recognition based on latent feature sets
formed through hierarchical agglomerative clustering using additive and multiplicative principles.
The compactness of the resulting feature sets is evaluated via a connectivity ratio based on a
hypersphere system. A computational experiment on genomic DNA data demonstrates that the
multiplicative principle outperforms the additive one, achieving a recognition accuracy of 96.15%
with only 5 latent features, confirming its effectiveness for solving dimensionality reduction problems
in data mining tasks.
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