Learning using granularity statistical invariants for classification
This work addresses computational bottlenecks in classification for large-scale datasets, though it appears incremental as it builds directly on the existing LUSI paradigm.
The paper tackles the high computational cost of invariant matrices in the LUSI learning paradigm for large-scale classification by introducing granularity statistical invariants, resulting in a new paradigm called LUGSI that enhances training speed and generalization, with experimental results showing faster training especially for large datasets.
Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems. However, the computation cost of invariant matrices in LUSI is high for large-scale datasets during training. To settle this issue, this paper introduces a granularity statistical invariant for LUSI, and develops a new learning paradigm called learning using granularity statistical invariants (LUGSI). LUGSI employs both strong and weak convergence mechanisms, taking a perspective of minimizing expected risk. As far as we know, it is the first time to construct granularity statistical invariants. Compared to LUSI, the introduction of this new statistical invariant brings two advantages. Firstly, it enhances the structural information of the data. Secondly, LUGSI transforms a large invariant matrix into a smaller one by maximizing the distance between classes, achieving feasibility for large-scale datasets classification problems and significantly enhancing the training speed of model operations. Experimental results indicate that LUGSI not only exhibits improved generalization capabilities but also demonstrates faster training speed, particularly for large-scale datasets.