Regularized information maximization
Algorithms AIMech:RegularizedInformationMaximization DRAFT
Aliases: RIM, Regularised information maximisation
A discriminative clustering framework that fits conditional class probabilities by maximizing empirical input–assignment mutual information penalized by classifier complexity, with linear and kernel softmax configurations.
Identity Notes & Concept Scope
RIM denotes Gomes et al.’s named clustering framework. This record keeps its linear, kernel, nonuniform-prior and semi-supervised configurations explicit. It differs from IMSAT’s augmentation-consistency regularizer and from IIC’s information between paired assignment variables. No normalized identity match was found across all 442 pinned AIO labels/scoped synonyms and 203 preceding native IDs/names/aliases. Ignored-inclusive word-delimited searches of both full checkouts returned 52 worktree and 75 main-checkout lines: existing component discussions and discovery/review references, plus unrelated ignored dependency text in main. Short acronyms were compared in normalized identities. Four nearest maintained mechanisms and eleven exact AIO candidate nodes with direct parent edges were inspected.
Claims & Grounded Evidence
1 Claim
Mechanism
#method
For N unlabeled inputs let p_theta(y|x_i) be conditional cluster probabilities and q(y)=N^−1 sum_i p_theta(y|x_i). Maximize H(q)−N^−1 sum_i H(p_theta(.|x_i))−R(theta). The linear configuration uses softmax(w_y^T x+b_y), R=lambda sum_y ||w_y||², and unpenalized biases, optimized with L-BFGS. Initialize by oversegmenting the data, fitting a multinomial logistic classifier to the resulting pseudo-labels, then optimizing the regularized information objective. Redundant categories can shrink toward zero marginal probability as their weights decay and biases become negative. The kernel configuration replaces linear scores with sum_i alpha_yi K(x_i,x)+b_y and penalizes RKHS squared norms alpha_y^T K alpha_y, with a regularization coefficient. A nonuniform-prior variant instead maximizes −mean conditional entropy−KL(q||D)−R for a chosen class prior D. A semi-supervised extension adds labeled log likelihood and weights the unlabeled information term separately.
⚠️ Curated Limitations & Negative Results
- The information objective is nonconcave. Finite L-BFGS optimization and a regularization sweep do not guarantee a global optimum or a true cluster count. Softmax probabilities are positive at finite parameters; exact class deletion is an ideal limiting description.
- The uniform empirical input distribution and class-balance preference implicit in marginal entropy may not fit the desired population. The nonuniform-prior variant changes the objective and requires compatible prior support.
- Kernel scores require an appropriate positive-semidefinite kernel for the stated RKHS penalty. Equation (8) omits the regularization coefficient while the following derivative includes it; this record makes that coefficient explicit.
- The paper reports ground-truth adjusted-Rand scores across regularization settings. Those plots are not evidence for an automatic label-free hyperparameter or true-cluster-count selection rule.
- Primary methods and declared experimental context were inspected; no RIM implementation, optimizer behavior or benchmark was reproduced.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:UnsupervisedClustering
Unsupervised Clustering
|
related_match | The unlabeled RIM configuration learns cluster assignments. The AIO target is the clustering task; this is an algorithm-to-task association, not an is-a assertion. The optional semi-supervised extension is stated separately. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
Ryan Gomes, Andreas Krause, Pietro Perona
|
primary paper
full_text
|
ID: https://papers.nips.cc/paper_files/paper/2010/hash/42998cf32d552343bc8e460416382dca-Abstract.html
sha256:9da080bfdbab…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T08:44:37Z
Updated: 2026-10-11T08:45:56Z
Source File:
data/curated/RegularizedInformationMaximization.yaml