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New scalable MADD algorithm tackles big-data classification challenges

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a scalable version of the Mean Absolute Difference of Distances (MADD) algorithm to address its computational limitations with large datasets. The original MADD algorithm, while effective in high-dimensional scenarios, suffers from quadratic complexity with respect to training sample size, making it impractical for big data. The proposed scalable version significantly reduces computational complexity by employing a representative set selection and leveraging Random Fourier Features for further speed-ups, enabling MADD's application to big data with a large number of observations. AI

IMPACT This research offers a more efficient method for classification tasks on large datasets, potentially improving the performance and applicability of distance-based algorithms in machine learning.

RANK_REASON The cluster describes a new methodology published in an academic paper on arXiv.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New scalable MADD algorithm tackles big-data classification challenges

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Annesha Ghosh, Adrija Saha, Soham Sarkar ·

    A scalable version of MADD for big-data classification

    arXiv:2607.08334v1 Announce Type: cross Abstract: Distance-based classifiers are very popular, and the Euclidean distance is one of the most commonly used metrics in distance-based classifiers. However, classifiers based on the Euclidean distance often suffer in high-dimensional …

  2. arXiv stat.ML TIER_1 English(EN) · Soham Sarkar ·

    A scalable version of MADD for big-data classification

    Distance-based classifiers are very popular, and the Euclidean distance is one of the most commonly used metrics in distance-based classifiers. However, classifiers based on the Euclidean distance often suffer in high-dimensional setups due to issues such as distance concentratio…

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