A Deterministic Binary Fingerprinting Framework with Zero-Trained Feature Extraction for Sparse Count Matrices

• URL: http://arxiv.org/abs/2607.14596v1
• Date: Thu, 16 Jul 2026 05:47:52 GMT
• Title: A Deterministic Binary Fingerprinting Framework with Zero-Trained Feature Extraction for Sparse Count Matrices
• Abstract summary: MMTB is a non-learned binary representation framework that requires no label supervision, model fitting, or gradient-based optimization.<n>Relative to dense float32 representations, MMTB fingerprints reduce memory by approximately 10-fold while providing fixed-width Hamming-indexable codes.
• Score: 5.730395615441126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse count matrices from single-cell transcriptomes to k-mer profiles and document-term frequencies are conventionally analyzed via PCA-reduced graph clustering or iterative optimization in continuous embedding spaces. We introduce MMTB, a deterministic non-learned binary representation framework that requires no label supervision, model fitting, or gradient-based optimization. Column-wise Min-Max normalization followed by fixed cutoffs maps each sample to a thermometer fingerprint whose Hamming distances show empirical correspondence with normalized L1 distances, with Pearson correlation approximately 0.92 on single-cell RNA-seq pairs. In a favorable three-cell-line mixture, the 3-threshold fingerprint achieves NMI of 0.99 at 188 bytes per cell. Under a fair Hamming nearest-neighbor graph plus Leiden readout, MMTB approaches PCA plus Leiden on this coarse task. On challenging tissue-like annotations, continuous pipelines often lead; PBMC Seurat NMI is 0.39 for MMTB versus 0.49 for Scanpy, underscoring that MMTB is suited for coarse-grained separation and resource-constrained deployments rather than fine-grained subtype discovery or as a general replacement for continuous embeddings. Relative to dense float32 representations, MMTB fingerprints reduce memory by approximately 10-fold while providing fixed-width Hamming-indexable codes. PCA30 embeddings and sparse CSR may be smaller; we do not claim universal compression. A label-free suitability score is provided as a deployment guideline, not a performance predictor.

Related papers

• Overfitting Mitigation via Singular Value Decomposition in Minimum Bayes Risk Decoding [53.9009465845701]
  We introduce SVD-MBR, which frames the pairwise utility matrix as a noisy information signal.<n> Experiments demonstrate that SVD-MBR successfully regularizes decoding, yielding substantial gains across a range of generalized metrics.
  arXiv  Detail & Related papers  (2026-09-01T12:11:29Z)
• Support Operation Factorization: Compositional Readout of Frozen Vision Encoders under Controlled Interventions [87.95757610455173]
  We introduce an injectively aligned leave-one-cell-out protocol over support x operation grids and SO-OPF.<n>We separate two questions that aggregate scores conflate: whether the carrier composes held-out bindings when the grid is known, and whether that grid can be recovered from flat cell labels.
  arXiv  Detail & Related papers  (2026-08-06T15:38:55Z)
• Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems [47.027290803102666]
  Normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes.<n>We model the normal law with a jointly learned latent representation plus explicit Gaussian-mixture mode clustering.<n>We evaluate under a deliberately fair protocol: raw point-wise metrics with no point adjustment, a trivial-detector difficulty split, prevalence-matched F1, and train-normal-only calibration.
  arXiv  Detail & Related papers  (2026-07-07T10:10:00Z)
• No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval [51.43543998583709]
  Single-stage Sparse Retrieval (SSR) is a paradigm shift that replaces expensive clustering with efficient sparse coding.<n>It reduces indexing time by 15x compared to ColBERTv2, halves retrieval latency, and simultaneously improves retrieval performance.
  arXiv  Detail & Related papers  (2026-05-28T15:53:34Z)
• Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models [0.2609784101826761]
  This paper studies the case where that search can be collapsed into one calibration step.<n> Linear operator-adaptive calibration gives comparable prediction quality to exhaustive preprocessing screening.
  arXiv  Detail & Related papers  (2026-05-13T14:23:00Z)
• Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models [0.3250525349446657]
  We evaluate how representation decisions affect downstream prediction after a shared one-epoch pretraining budget.<n>We train 28 matched transformers on MIMIC-IV and evaluate them on 30 clinical outcomes.
  arXiv  Detail & Related papers  (2026-04-18T01:38:47Z)
• Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyML [0.0]
  We present HYPER-TINYPW, a compression-as-generation approach that replaces most stored PW weights with generated weights.<n>A shared micro-MLP synthesizes PW kernels once at load time from tiny per-layer codes, caches them, and executes them with standard integer operators.<n>It preserves commodity MCU runtimes and adds only a one-off cost.
  arXiv  Detail & Related papers  (2026-03-26T01:08:52Z)
• Deterministic Fuzzy Triage for Legal Compliance Classification and Evidence Retrieval [0.0]
  Legal teams increasingly use machine learning to triage large volumes of contractual evidence.<n>Many models are opaque, non-deterministic, and difficult to align with frameworks such as HIPAA or NERC-CIP.<n>We study a simple, reproducible alternative based on deterministic dual encoders and transparent fuzzy triage bands.
  arXiv  Detail & Related papers  (2026-03-08T00:31:34Z)
• Identity-Link IRT for Label-Free LLM Evaluation: Preserving Additivity in TVD-MI Scores [3.959606869996232]
  We show that averaging TVD-MI's binary trials yields centered-probability scores with additive structure suitable for item-response theory (IRT) without nonlinear link functions.<n>We derive this clipped-linear evaluations from Gini entropy, yielding a box-constrained least-squares formulation that handles boundary saturation.
  arXiv  Detail & Related papers  (2025-10-16T17:59:25Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Pruning Neural Belief Propagation Decoders [77.237958592189]
  We introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning. We achieve performance within 0.27 dB and 1.5 dB of the ML performance while reducing the complexity of the decoder.
  arXiv  Detail & Related papers  (2020-01-21T12:05:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.