AIMech AI

Parameter-Expanded Expectation Maximization

Algorithms AIMech:ParameterExpandedExpectationMaximization DRAFT
Aliases: PX-EM, Parameter-Expanded EM
An expectation-maximization construction that fits an expanded complete-data model with auxiliary parameters, reduces its result to the original observed-data parameterization through a likelihood-preserving map, and resets the auxiliary parameters before the next iteration.
Identity Notes & Concept Scope PX-EM changes the complete-data representation without enlarging the original observed-data model family. Auxiliary parameters are not independently identified from the observed data. It is deterministic likelihood fitting, distinct from parameter-expanded data-augmentation sampling, generic model overparameterization, ECM constrained updates and transformation-based training-data augmentation. No normalized identity match was found across all 442 pinned AIO labels and scoped synonyms and 463 preceding native IDs, names and aliases. Fixed-string searches of worktree and main included ignored and hidden files, yielding 52 and 53 matched lines respectively. All 41 distinct contextual passages across 105 line instances and nine checkout-qualified paths were read; these concern prior discovery references and factor-analysis evidence, plus an unrelated ignored URI-template parameter-expansion string. They are not existing identities for these three algorithms. All 223 semantic label/synonym rows were read. Fifteen exact annotated AIO CLASS nodes and outgoing edges were inspected: fourteen verified unchanged after prior reading and DataAugmentation newly read. Twenty-five contextual YAMLs comprise fifteen AIO and ten native records; twenty are unchanged verified prior readings, two are hash-verified final records fully read in the preceding cohort, and three were fully read here. General EM, tempered EM, Gaussian factor models, PPCA mixtures, Gibbs/Metropolis sampling, specialized deep Bayesian data augmentation and alternating Newton fitting do not identify these particular conditional/expanded EM procedures. Absence is bounded to the pinned snapshot, maintained corpus and inspected neighborhood.

Claims & Grounded Evidence

4 Claims
Mechanism #expand-fit-reduce-reset
Specify an expanded complete-data density p_X(y_com|theta_star,alpha) that recovers the original augmentation at alpha_0 and whose observed density equals the original density at theta=R(theta_star,alpha). At each iteration embed the current theta with alpha_0, compute expanded conditional sufficient moments, maximize expanded Q over the expanded parameters, reduce by R, and reset the auxiliary parameter to alpha_0 for the next E step. The observed-density equality makes expanded EM ascent transfer to the original likelihood.
Mechanism #fixed-degrees-t-expansion
For a p-dimensional t distribution with fixed positive degrees of freedom nu, old latent weights are w_i=(nu+p)/(nu+delta_i), with delta_i the old scatter-based squared Mahalanobis distance. Ordinary EM uses the weighted mean and scatter sum_i w_i(y_i-mu_new)(y_i-mu_new)^T/N. Expanding the mean of the latent gamma scale yields alpha_new=sum_i w_i/N. Dividing the expanded scatter by alpha_new reduces to the original t model, giving denominator sum_i w_i instead of N; the next iteration resets alpha to one. This normalization follows from the stated expansion and reduction.
Mechanism #scalar-normal-example
For y|z~Normal(z,1), z~Normal(theta,sigma_squared), with known positive sigma_squared, ordinary EM updates theta to m=(theta_old+sigma_squared*y)/(1+sigma_squared). Expanding the conditional observation mean to z+alpha gives theta_star_new=m and alpha_new=y-m. The reduction theta_star+alpha returns theta_new=y in one iteration, while ordinary EM contracts its error by 1/(1+sigma_squared). This exact example does not establish one-step convergence for other expansions.
Limitation #local-rate-boundary
The information-matrix comparison linearizes at a converged likelihood solution under the stated differentiability, invertibility and positive-information conditions. It orders the slowest local linear convergence direction relative to the parent EM and permits equality. The paper’s global rate terminology refers to the extremal local direction, not convergence from arbitrary initializations. It does not order every finite iterate or total computational time.

⚠️ Curated Limitations & Negative Results

Artificial Intelligence Ontology (AIO) Grounding

Mapped AIO Classes
AIO Target Class Mapping Relation Mapping Rationale
AIO:MachineLearningTask
Machine Learning Task
related_match This is an explicit likelihood-fitting algorithm used to learn parameters from data. The pinned target denotes a learning process/task, so the link is algorithm-to-task relatedness, not equivalence or a subclass assertion. Latent-data augmentation and auxiliary parameter expansion do not identify the pinned transformation-based DataAugmentation term.

Evidence Sources & Primary Literature

1 Source
Source ID Publication / Document Type / Access Reference & Checksum
method-source
Chuanhai Liu, Donald B. Rubin, Ying Nian Wu
primary paper
full_text
ID: https://faculty.stat.ucla.edu/ywu/research/papers/PXEM.pdf
sha256:ab0e7c8503b3…

Curation Provenance

Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-12T03:11:01Z
Updated: 2026-10-12T03:11:01Z
Source File: data/curated/ParameterExpandedExpectationMaximization.yaml