arXiv:2605. 11428v2 Announce Type: replace Abstract: Exploratory analysis of high-dimensional data rarely stops at a single embedding.
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The paper studies Online Kernel Supervised Principal Component Analysis (OKSPCA), which uses random features and an Adam-style orthonormal basis update to optimize a supervised spectral objective. It shows that accurate optimization of this objective does not guarantee accurate population subspace recovery or improved predictive performance, and it provides theoretical results on consistency, concentration, and perturbation of the estimator. Empirical experiments on six benchmarks reveal that replacing the tracker with the exact empirical target does not significantly change regression deficits, while classification-rank models capture most of the terminal objective energy but can exhibit substantial geometric deviation; sample-size studies further separate empirical accuracy from population recovery. The diagnostics also compare computational trade-offs, indicating that exact on-request computation can be faster in classification settings, whereas Adam saves time relative to full thin‑SVD in some dense regression requests, despite persistent geometric error.
arXiv:2510. 00566v4 Announce Type: replace-cross Abstract: Approximate Nearest-Neighbor Search (ANNS) pipelines for high-dimensional neural embeddings spend the bulk of their query time in candidate verification, making it the primary bottleneck in the search process.
arXiv:2608. 11269v1 Announce Type: cross Abstract: Omics datasets, particularly single-cell RNA sequencing data, are high-dimensional, sparse, noisy, and dominated by zero values, making faithful low-dimensional representation challenging.
arXiv:2606. 04451v1 Announce Type: new Abstract: Neighbor embedding algorithms reveal correlations in high-dimensional data by constructing an equivalent graph representation in a lower-dimensional space.
The paper investigates how adjusting the sharpness of t‑SNE’s affinity matrix influences neighborhood preservation across scales. By applying a row‑wise power transform parameterized by γ, the authors can smooth or sharpen each row while keeping sparsity and rank order intact, effectively rescaling the Gaussian bandwidth and altering local perplexities. Experiments show that sharpening enhances the retention of the very nearest neighbors, whereas smoothing improves the preservation of broader local neighborhoods, outperforming existing multiscale affinity methods in the mid‑local range.
arXiv:2609.38901v1 Announce Type: new Abstract: Representer explanations rank the training landmarks that most influence a self-supervised representation. At scale, this ranking rests on up to four s...