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The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
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CompleMatch: Mejora de la Clasificación Semi-Supervisada de Series Temporales con Complementariedad

Zhen Liu, Kun Zeng, Qianli Ma

    IEEE transactions on pattern analysis and machine intelligence
    |December 15, 2025
    PubMed
    Resumen
    Este resumen es generado por máquina.

    CompleMatch mejora la clasificación semi-supervisada (SSC) de series temporales combinando datos temporales y de frecuencia. Este novedoso enfoque mejora la precisión del modelo con datos etiquetados limitados, superando a los métodos existentes.

    Palabras clave:
    clasificación semi-supervisadaseries temporalesaprendizaje profundodominio temporaldominio de frecuenciaaprendizaje contrastivo

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    Área de la Ciencia:

    • Aprendizaje Automático
    • Ciencia de Datos
    • Procesamiento de Señales

    Sus antecedentes:

    • La Clasificación Semi-Supervisada (SSC) aprovecha los datos no etiquetados para mejorar el rendimiento del modelo cuando las muestras etiquetadas son escasas.
    • Los métodos existentes de SSC de series temporales se basan principalmente en dependencias temporales, que pueden ser sensibles al ruido y pueden pasar por alto la periodicidad de las características globales.

    Objetivo del estudio:

    • Introducir CompleMatch, un novedoso marco de SSC de series temporales que utiliza información complementaria de los dominios temporal y de frecuencia.
    • Mejorar el aprendizaje a partir de datos no etiquetados integrando diversas representaciones de datos.

    Principales métodos:

    • CompleMatch emplea un paradigma de co-entrenamiento con dos redes neuronales profundas entrenadas simultáneamente, una para vistas del dominio temporal y otra para el dominio de frecuencia.
    • Las pseudo-etiquetas generadas mediante la propagación de etiquetas guían el entrenamiento de cada red, explotando la naturaleza complementaria de las representaciones temporal-frecuencial.
    • Un módulo de aprendizaje contrastivo temporal-frecuencial integra señales supervisadas y auto-supervisadas para mejorar la calidad de las pseudo-etiquetas y la discriminabilidad de la representación.

    Principales resultados:

    • CompleMatch supera significativamente los métodos de vanguardia en tareas de SSC de series temporales.
    • Los estudios de ablación y las visualizaciones confirman la efectividad de la estrategia propuesta de aprendizaje complementario temporal-frecuencial.
    • El marco demuestra una robustez y un rendimiento mejorados, particularmente en condiciones de datos etiquetados limitados.

    Conclusiones:

    • El marco propuesto CompleMatch aprovecha eficazmente la información temporal y de frecuencia complementaria para una SSC robusta de series temporales.
    • La integración de diversas representaciones de datos y el aprendizaje contrastivo mejoran el rendimiento del modelo y el poder discriminatorio.
    • CompleMatch ofrece un avance prometedor para el aprendizaje semi-supervisado en el análisis de series temporales.