| Summary: | We offer a unique Internet framework for multimedia learning, which at the same time teaches optimal metrics in each individual way as well as the optimal combination of multidimensional metrics through effective learning and online learning. This article examines a unique framework for learning Metric Learning, which teaches distance measures multimedia data or multiple types of features with an effective and scalable online learning plan. OMDML benefits from the benefits of online learning methodologies for high quality and scalability towards learning tasks on a large scale. Like the classic classical method of online learning, the Perceptions formula simply updates the form by adding an incoming instance of fixed weight when it is incorrectly classified. Although many of the DML algorithms are suggested in the literature, most of the current DML methods generally match the DML monochrome by the fact that they are familiar with the distance scale on the feature type or in the feature space simply combining multiple types of different properties together. To help reduce the cost of arithmetic, we propose a minimal DML formula, which eliminates the need for very accurate semi-precise projections, thus providing a large DML calculation cost in high-dimensional data.
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