Projected Dimensionality Reduction
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Homology-Preserving Dimensionality Reduction via Adaptive Mapper and Landmark Isomap
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ASH: Asymmetric Scalar Hashing With Learned Dimensionality Reduction for High-Fidelity Vector Quantization
arXiv:2606.07870v1 Announce Type: new Abstract: For a long time, additive quantizers, such as product quantization, have been considered the gold standard in terms of accuracy and efficiency. Recently, scalar quantization has re-emerged from the depths of history with a new wave of data-agnostic techniques. Inscribed in this general framework, we turn our attention to data-driven methods, showing that new highs in recall and speed can be achieved by reducing the number of dimensions while...
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
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Riemannian Stochastic Optimization for Sufficient Dimension Reduction
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Sequential Subspace Mode Adaptation for the Reduced-Order Homogenization of Dissipative Microstructures using E3C Hyper-Reduction
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Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures
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Whole-genome duplication shaped cell-type evolution in the vertebrate brain
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No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval
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