arXiv Machine Learning

Terminal Dimension Reduction for Time Series with Applications

arXiv:2607. 09490v1 Announce Type: cross Abstract: Terminal embeddings have emerged as a powerful tool for dimension reduction.

arXiv Machine Learning

Aug 17

Connected Subspace Clustering: Hardness, a Scalable Heuristic, and an Application to Sea Level Geodesy

arXiv:2608. 14215v1 Announce Type: new Abstract: Constrained optimization extends classical optimization by integrating side information, making it widely applicable across scientific and engineering domains.

By Johanna Hillebrand, Jan H\"ockendorff, J\"urgen Kusche, Kelin Luo, Heiko R\"oglin, Melanie Schmidt, Christian Sohler, Bernd Uebbing

arXiv Machine Learning

Jul 7

Dimension Reduction for Curves: Simplified and Generalized

arXiv:2607. 03112v1 Announce Type: cross Abstract: We revisit random projections for reducing the dimension of high-dimensional polygonal curves.

By Matthijs Ebbens, Jie Lu, Alexander Munteanu

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arXiv Statistics ML

Aug 25

Spectral partitioning for $k$-block averaging kernels of finite Markov chains

arXiv:2608.21466v1 Announce Type: new Abstract: We develop spectral algorithms for selecting state-space partitions that define averaging kernels for finite, ergodic and reversible Markov chains. For...

By Michael C. H. Choi, Youjia Wang

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arXiv Machine Learning

Sep 4

Anisotropic View Distance Metric for High-Dimensional Data: Theory, Geometry, and Fast Computation

The paper introduces View distance, a novel metric that projects high‑dimensional data onto all pairwise two‑dimensional planes and sums the Euclidean distances across these projections. It satisfies metric axioms, couples features, suppresses redundancy, and captures anisotropic geometry. To make it scalable, the authors propose a plane‑selection strategy using iterative Maximum Weight Matching, reducing complexity from ω(n²) to ω(k) and demonstrating competitive performance on twelve datasets.

By Yiqun Zhang, Hou-biao Li

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arXiv Machine Learning

Sep 10

Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling

arXiv:2609.06394v1 Announce Type: cross Abstract: Massive datasets in modern machine learning have made data reduction a central challenge, particularly for clustering tasks where memory and computat...

By Diptarka Chakraborty, Satyaki Mukherjee, Gaurav Vallabhdas Revankar, Hoang-Son Tran

benchmarks

arXiv Machine Learning

Jul 3

Scalable and Distributed Silhouette Approximation

arXiv:2607. 01993v1 Announce Type: cross Abstract: The silhouette is one of the most widely used measures to assess the quality of a $k$-clustering of a dataset of $n$ elements.

By Ilie Sarpe, Federico Altieri, Andrea Pietracaprina, Geppino Pucci, Fabio Vandin

benchmarks

arXiv Machine Learning

Jun 16

Characterizing Admissible Objective Functions for Hierarchical Clustering

arXiv:2604. 23628v2 Announce Type: replace-cross Abstract: Hierarchical clustering is a fundamental task in data analysis, but classical methods have long lacked a principled objective function.

By Ryuki Tsukuba, Kazutoshi Ando

arXiv Machine Learning

Jun 9

Orthogonality and Dimensionality in Airline Cluster Analysis using PCA and Kernel PCA

arXiv:2606. 08322v1 Announce Type: new Abstract: To characterize the US airline profit cycles from 1995 to 2020, the authors of Renold et al.

By Andreas Schlapbach

Hugging Face Trending Papers

Sep 1

Sierpiński--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximation

This paper introduces the Sierpiński-Knopp (SK) Wasserstein distance, a fast metric between persistence diagrams. The SK-Wasserstein distance, denoted $d_{\mathrm{SK}}$, maps diagram points and their...

rag computer-vision benchmarks

arXiv Machine Learning

Jun 16

Active Learning with Low-Rank Structure for Data Selection

arXiv:2606. 16045v1 Announce Type: new Abstract: In the data selection problem, the objective is to choose a small, representative subset of data that can be used to efficiently train a machine learning model.

By Vincent Cohen-Addad, Sasidhar Kunapuli, Vahab Mirrokni, Mahdi Nikdan, David P. Woodruff, Samson Zhou

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arXiv Machine Learning

Jul 7

Sensitivity Sampling with Predictions for k-Means Clustering

arXiv:2607. 04949v1 Announce Type: new Abstract: We study the problem of k-means clustering on large datasets.

By Cristian Boldrin, Fabio Vandin

benchmarks

arXiv Machine Learning

Jun 5

Central Description Length (CDL) Clustering Validation Index

arXiv:2606. 05230v1 Announce Type: cross Abstract: Selecting a clustering algorithm and its hyperparameters without labels is a common difficulty in engineering machine learning pipelines that work with unsupervised analysis of sensor, image, or process data.

By Mahdi Shamsi, Soosan Beheshti

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