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Algoritmo robusto de selección de características sin supervisión basado en un gráfico de anclaje difuso

Zhouqing Yan1, Ziping Ma1,2, Jinlin Ma3

  • 1School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Un nuevo algoritmo de anclaje difuso (FWFGFS) mejora la selección de características sin supervisión al incorporar información difusa de datos. Este método mejora la precisión del agrupamiento y reduce el impacto del ruido para una mejor selección del subconjunto de características.

Palabras clave:
gráfico borrosoponderación difusaTrifactorización ortogonalSelección de características sin supervisión

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Área de la Ciencia:

  • Aprendizaje automático
  • Minería de datos
  • Reconocimiento de patrones

Sus antecedentes:

  • La selección de características sin supervisión identifica subconjuntos de características óptimas sin etiquetas.
  • Los métodos existentes luchan con la información y el ruido difusos de los datos, lo que afecta al modelado de la estructura del clúster.
  • El error cuadrado en la reconstrucción exacerba la sensibilidad al ruido en los enfoques actuales.

Objetivo del estudio:

  • Proponer un algoritmo de selección de características robusto y sin supervisión, FWFGFS, utilizando gráficos de anclaje difusos.
  • Abordar las limitaciones de los métodos existentes mediante el modelado eficaz de estructuras de clúster difusas y la mitigación del ruido.
  • Mejorar la precisión y la robustez de la selección de características en datos sin etiquetar.

Principales métodos:

  • Desarrollar un mecanismo de aprendizaje de gráficos de anclaje difuso con distribuciones de membresía difusas para asignaciones de clústeres blandos.
  • Introducir un mecanismo de ponderación difuso adaptativo para reducir el ruido y los errores de las características redundantes.
  • Aplique la trifactorización ortogonal a la representación de baja dimensión para los centros de racimo independientes.

Principales resultados:

  • FWFGFS modela efectivamente las relaciones de vecindad difusas, mejorando la precisión del agrupamiento.
  • El mecanismo de ponderación adaptativo reduce la interferencia del ruido en la selección de características.
  • Los resultados experimentales demuestran mejoras significativas en la precisión promedio de agrupación (5,68% 13,79%) en comparación con los métodos de última generación en 12 conjuntos de datos.

Conclusiones:

  • El FWFGFS ofrece un enfoque sólido y preciso para la selección de características sin supervisión mediante el aprovechamiento de información difusa.
  • Los mecanismos propuestos mejoran el modelado de la estructura del clúster y la resistencia al ruido.
  • El FWFGFS representa un avance significativo en la selección de características para el análisis de datos sin etiqueta.