MULTI SVDD BASE ON PSUDO DENSITY

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MULTI SVDD BASE ON PSUDO DENSITY

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Multi-SVDD (Multi-class Support Vector Data Description) based on Pseudo Density is an advanced approach in machine learning, particularly useful for anomaly detection and one-class classification tasks. Traditional SVDD methods focus on defining a boundary around normal data points to identify outliers, but extending this to multi-class scenarios introduces complexity.

By incorporating Pseudo Density, this method enhances the detection capability by estimating the underlying data distribution without requiring strict parametric assumptions. Instead of relying solely on geometric boundaries, the model leverages density-based insights to differentiate between multiple classes or detect anomalies more robustly.

This approach is especially valuable in domains where data may belong to multiple categories, yet anomalies must still be identified—such as fraud detection, fault diagnosis, or medical imaging. The pseudo-density estimation helps in refining decision boundaries, making the model more adaptable to real-world data variations.

Key advantages include improved generalization across multiple normal classes and better handling of overlapping distributions. However, challenges may arise in computational efficiency when scaling to high-dimensional datasets. Future research directions could explore hybrid methods combining deep learning for density estimation.