Tag
The paper presents a unified geometric framework for understanding differentiable embeddings like t-SNE and UMAP, showing that existing diagnostics derive from a single object and proposing new curvature-based and integral-path trust measures.
This paper studies using Whisper for Persian speech emotion recognition, showing PCA-based dimensionality reduction improves performance and efficiency, while ASR fine-tuning offers only modest gains.
This paper systematically compares tabular foundation models (TabPFN) with classical regression approaches across 85 soil spectroscopy tasks, finding that TabPFN combined with PLS-derived features achieves the best predictive performance from field-scale to global spectral libraries.
FADEx introduces a novel local per-instance feature attribution method for explaining dimensionality reduction techniques, using Taylor expansion and Singular Value Decomposition to provide model-agnostic explanations and distortion analysis.
FloDR is a dimensionality reduction method based on a normalising flow that creates an invertible embedding, preserving both local and global structure while providing diagnostic tools like conditional spread and hidden contrast.
CLOE is a new semi-supervised anomaly detection method combining an autoencoder with a Christoffel Function-based detector, using a novel loss function to improve representation learning. It achieves state-of-the-art results on high-dimensional tabular data while maintaining simplicity.
This paper proposes incorporating symmetries into affinity kernels for spectral embedding, proving convergence of invariant graph Laplacians on quotient manifolds with improved sample complexity.
This paper introduces ODIN, a novel autoencoder architecture that enforces orthogonality and importance ordering of latent dimensions, recovering PCA-like interpretability in a fully non-linear regime. The method integrates geometric constraints into the training objective, theoretically grounded and empirically validated on synthetic and real-world datasets.
Proposes a method to embed graphs in high-dimensional space and search for informative 2D viewpoints that optimize aesthetic and readability metrics, enabled by a novel differentiable surrogate for edge crossings. Introduces an interactive system, DataFly, for exploring multiple candidate viewpoints.
Proposes GraphDR-LinUCB, a method for contextual bandits with graph-structured arms that projects features onto the graph's low-frequency spectral subspace. Achieves the first regret bound for spectral-projection-based contextual bandits and demonstrates 15x regret reduction on real datasets over full-dimensional LinUCB.
This paper presents exact dimensionality reductions using Schur complement and Sylvester's determinant identity to reduce computational complexity from O(N^3) to O(k^3+N^2k) per step for non-smooth NML estimation, achieving over 14,000x speedup while maintaining numerical precision.
Proposes Distance-Adaptive Representation (DAR) which reduces key-value dimensionality for distant tokens while preserving full dimensionality for nearby tokens, improving KV cache efficiency without performance loss.
This study reveals that LLM text embeddings are hijacked by high-frequency tokens (e.g., periods, articles) and proposes EmbedFilter, which performs SVD on the unembedding matrix and subtracts the projection component to release true semantics, achieving zero-training-cost dimensionality reduction and retrieval efficiency gains.
The paper identifies that LLM text embeddings overly express high-frequency uninformative tokens and proposes EmbedFilter, a linear transformation that filters out this subspace to improve semantic representations and enable dimensionality reduction.
A short mathematical write-up on Principal Component Analysis (PCA), explaining the concept and its applications.
ScaleMAP is a new nonlinear dimensionality reduction method that preserves local density and neighborhood structure by rescaling embedding distances based on original-space local radii, achieving better density preservation than DensMAP while maintaining UMAP-level neighborhood preservation.
Proposes DIVE, a compression adapter for embedding dimensionality reduction that uses self-limiting gradient updates and head-wise NT-Xent contrastive loss to prevent overfitting on small datasets, outperforming existing methods on BEIR benchmarks.
This paper proposes a hybrid quantum-classical workflow for plant phenomics classification under small-data regimes, using supervised latent restructuring (PCA + LDA) to improve geometric separability before quantum kernel alignment. Experiments show improved separability but highlight compression trade-offs and the difficulty of achieving strong quantum performance.
This paper introduces an unsupervised framework for modeling acquisition-related variability in structural connectomes using hybrid latent space modeling, eliminating the need for manual capacity tuning by architecturally annealing encoder outputs.
This paper proposes Spectra, a method using spectral occupancy to analyze and control the realized capacity of latent graph models, arguing that rank is not equivalent to model capacity.