# Multi-Integration of Labels across Categories for Component Identification (MILCCI)

arXiv: https://arxiv.org/abs/2602.04270 · HTML: https://mlsanity.com/paper/2602.04270/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters](https://mlsanity.com/paper/1410.6031/) (2014)
- [Distributed Flexible Nonlinear Tensor Factorization](https://mlsanity.com/paper/1604.07928/) (2016)
- [Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition](https://mlsanity.com/paper/2207.02446/) (2022)
- [Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics](https://mlsanity.com/paper/2408.16862/) (2024)
- [SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States](https://mlsanity.com/paper/2306.04817/) (2023)
- [CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations](https://mlsanity.com/paper/2405.17395/) (2024)
- [Modulation of metastable ensemble dynamics explains the inverted-U relationship between tone discriminability and arousal in auditory cortex](https://mlsanity.com/paper/2404.03902/) (2024)

## Built on by (most cited first, 1 of 1)

- [Neighbor Embedding for High-Dimensional Sparse Poisson Data](https://mlsanity.com/paper/2604.16932/) (2026)

## Abstract (arXiv)

Many fields collect large-scale temporal data through repeated measurements (trials), where each trial is labeled with a set of metadata variables spanning several categories. For example, a trial in a neuroscience study may be linked to a value from category (a): task difficulty, and category (b): animal choice. A critical challenge in time-series analysis is to understand how these labels are encoded within the multi-trial observations, and disentangle the distinct effect of each label entry across categories. Here, we present MILCCI, a novel data-driven method that i) identifies the interpretable components underlying the data, ii) captures cross-trial variability, and iii) integrates label information to understand each category's representation within the data. MILCCI extends a sparse per-trial decomposition that leverages label similarities within each category to enable subtle, label-driven cross-trial adjustments in component compositions and to distinguish the contribution of each category. MILCCI also learns each component's corresponding temporal trace, which evolves over time within each trial and varies flexibly across trials. We demonstrate MILCCI's performance through both synthetic and real-world examples, including voting patterns, online page view trends, and neuronal recordings.