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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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    This study introduces a robust deep matrix factorization framework for high-dimensional gene data. The novel dual-angle feature approach enhances tumor classification accuracy and robustness against noise.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • High dimensionality and noise in complex data pose challenges for dimensionality reduction.
    • Deep matrix factorization offers potential for effective data dimensionality reduction.

    Purpose of the Study:

    • To propose a novel robust and effective deep matrix factorization framework for high-dimensional gene data.
    • To improve the effectiveness and robustness of tumor classification using gene expression data.

    Main Methods:

    • A robust deep matrix factorization (RDMF) model was developed for feature learning in noisy data.
    • A double-angle feature (RDMF-DA) was constructed by combining RDMF and sparse features for comprehensive gene information.
    • A gene selection method based on sparse representation (SR) and gene coexpression was used for feature purification.

    Main Results:

    • The proposed framework effectively handles high-dimensional and noisy gene expression data.
    • The dual-angle feature construction captures more comprehensive information, improving classification.
    • Feature purification using SR and gene coexpression minimizes the impact of redundant genes.

    Conclusions:

    • The novel deep matrix factorization framework demonstrates superior performance in high-dimensional tumor classification.
    • The RDMF-DA method with feature purification offers a robust solution for analyzing complex gene expression data.