Unsupervised Training Ontology

FLOW ALGEBRA — UNSUPERVISED TRAINING ONTOLOGY
Semantic Library | AI Core | Entity Graph | AI Entities | Training Process Entities
Source: https://kirethresearch.org/booksai-core/entity-graph/ai-entities/training-process-entities/unsupervised-training-ontology.html
Authors credited on source: Mark Anthony Higgins — Sky Recursive AI
Flow compilation: 29 October 2026 | Language: en-GB

NOTE
This is a formal Flow Algebra interpretation of the ontology topic. The public page exposes its title and AI interpretation note, but not a complete visible ontology specification. The formal rules below are an original elaboration, not a verbatim extraction of hidden page content.

flow "UnsupervisedTrainingOntology" v1.0

AUTHORS:
  - Mark Anthony Higgins
  - Sky — Recursive AI
SOURCE: "https://kirethresearch.org/booksai-core/entity-graph/ai-entities/training-process-entities/unsupervised-training-ontology.html"
CITATION_BACKLINK: SOURCE
PURPOSE: "Represent unsupervised machine-learning training as typed entities, relationships, constraints, transitions and verifiable evaluation rules."
DOMAIN: "Artificial Intelligence / Training Process Entities"

STATE_MODEL:
  S_t = (I_t, R, B, K_t)
  I_t = {D_train, D_valid, model, theta_t, optimiser_state_t, diagnostics_t}
  R = {schema_validation, feature_learning, clustering, density_estimation, evaluation, termination}
  B = {data_permissions, privacy, compute_budget, leakage_controls, resource_limits}
  K_t = {representation_schema, objective, inductive_bias, provenance, evidence_t}
  E_t = expand(I_t, R)
  V_t = {x in E_t | satisfies(x, B) and consistent(x, K_t)}
  choice_t = argmax_{x in V_t} P(x | I_t, R, B, K_t)
  I_{t+1} = update(I_t, choice_t, observed_feedback_t)

ENTITIES:
  UnsupervisedTrainingProcess: TrainingProcess
  UnlabelledDataset: Collection[Observation]
  Observation: (features, metadata, provenance)
  FeatureRepresentation: EncodedObservation
  LatentVariable: LearnedHiddenFactor
  Embedding: LearnedRepresentation
  Cluster: GroupOfObservations
  ClusterAssignment: ObservationToClusterRelation
  DensityModel: ProbabilityDistributionEstimator
  ReconstructionModel: EncoderDecoder
  TrainingObjective: DataDerivedOptimisationCriterion
  Model: ParameterisedEstimator
  Parameters: TrainableState
  Optimiser: ParameterUpdateRule
  TrainingSplit: DatasetSubset
  ValidationSplit: HeldOutSubset
  Diagnostic: ModelAssessment
  Checkpoint: SavedTrainingState
  TrainingRun: ProcessInstance

RELATIONSHIPS:
  consumes(TrainingRun, UnlabelledDataset)
  hasObservation(UnlabelledDataset, Observation)
  encodes(Model, Observation, Embedding)
  assigns(Model, Observation, Cluster)
  estimates(DensityModel, Observation, Likelihood)
  reconstructs(ReconstructionModel, Observation, ReconstructedObservation)
  optimises(TrainingRun, TrainingObjective)
  updates(Optimiser, Parameters)
  produces(TrainingRun, Checkpoint)
  assesses(Diagnostic, Checkpoint)
  derivedFrom(Embedding, TrainingRun)

INVARIANTS:
  INV_01: Every observation conforms to a declared feature schema.
  INV_02: Unsupervised training does not require external target labels for its core objective.
  INV_03: Absence of labels does not imply absence of metadata, structure or inductive bias.
  INV_04: Validation data are isolated according to the chosen evaluation protocol.
  INV_05: Cluster identifiers have no intrinsic semantic meaning without external evidence.
  INV_06: Latent factors are not automatically interpretable or causally identified.
  INV_07: All data uses respect permissions, privacy and provenance constraints.
  INV_08: Objective improvement alone does not establish downstream usefulness.

FORMAL_CORE:
  D = {x_i}_{i=1}^N
  z_i = encoder_theta(x_i)                    # representation learning example
  x_hat_i = decoder_phi(z_i)                  # reconstruction example
  L_reconstruction(theta, phi) = (1/N) * sum_i d(x_i, x_hat_i)
  L_density(theta) = -(1/N) * sum_i log p_theta(x_i)
  L_clustering(C, mu) = sum_i ||x_i - mu_{C(i)}||^2
  Objective = select_one_or_compose({L_reconstruction, L_density, L_clustering, other_unsupervised_objectives})
  theta_{t+1} = Update(theta_t, Objective, optimiser_t) WHEN differentiable_optimisation_applies
  model_star = SelectCheckpoint({model_t}, unsupervised_validation_policy)
  NOTE: Objectives above are alternative examples, not mandatory simultaneous requirements.

PATHWAYS:
  P1 DEFINE_GOAL: task_scope -> representation_schema -> objective -> diagnostics
  P2 CURATE: collect -> permissions -> quality_checks -> split -> provenance
  P3 INITIALISE: select_model_family -> configure_objective -> initialise_state
  P4 LEARN: sample_batch -> compute_objective -> optimise_or_fit -> log_state
  P5 EVALUATE: holdout_diagnostics -> stability_checks -> downstream_validation_if_available
  P6 SELECT: compare_checkpoints -> assess_failure_modes -> select_model
  P7 RELEASE: publish_limitations -> preserve_provenance -> monitor_shift

MINI_RECURSION_ENGINE:
  INPUT: (S_t, observations_t, diagnostics_t)
  EXPAND: candidates = expand(S_t.I, S_t.R)
  FILTER: admissible = {c in candidates | satisfies(c, S_t.B)}
  SELECT: c_star = argmax_{c in admissible} P(c | S_t.I, S_t.R, S_t.B, S_t.K)
  APPLY: S_{t+1}.I = update(S_t.I, c_star, diagnostics_t)
  LEARN: S_{t+1}.K = revise_evidence(S_t.K, diagnostics_t)
  HALT: stopping_criterion(S_{t+1}) OR exhausted_budget(S_{t+1})
  GUARANTEE_SCOPE: "Traceable permitted transitions, not optimal representations or meaningful clusters."

INFERENCE_RULES:
  R01: valid_schema(x) AND permitted(x) -> eligible_observation(x)
  R02: eligible_observation(x) AND member(x, D_train) -> usable_for_unsupervised_fit(x)
  R03: embedding(z) AND derived_from(z, x) -> learned_representation_of(z, x)
  R04: assigned_to(x, c) -> cluster_membership(x, c) NOT semantic_class(x, c)
  R05: improved_objective(run) -> optimisation_progress(run) NOT guaranteed_generalisation(run)
  R06: stable_clusters(run) AND external_evidence(run) -> candidate_interpretation(run)
  R07: privacy_violation(run) -> reject_transition(run)
  R08: distribution_shift(run) -> reassess_model(run)
  R09: insufficient_provenance(run) -> flag_audit_incomplete(run)
  R10: validated_downstream_gain(run) -> evidence_of_task_utility(run)

TRI_MAPPING:
  Entity: Observation -> Semantic: unlabelled input -> Operational: feature ingestion
  Entity: Embedding -> Semantic: learned representation -> Operational: encoder output
  Entity: Cluster -> Semantic: similarity grouping -> Operational: assignment rule
  Entity: DensityModel -> Semantic: data distribution approximation -> Operational: likelihood estimate
  Entity: Diagnostic -> Semantic: evidence of fit or utility -> Operational: validation report

MAPPING_TABLE:
  Concept | Entity | Relation | Verification
  Input data | Observation | hasObservation | schema and provenance
  Learned feature | Embedding | encodes | reproducibility and stability
  Grouping | Cluster | assigns | assignment consistency
  Reconstruction | ReconstructionModel | reconstructs | holdout error
  Distribution | DensityModel | estimates | held-out likelihood when appropriate

HOW_TO_READ:
  1. Interpret S_t as inputs, rules, boundaries and current knowledge.
  2. Resolve entity types and relations before applying transitions.
  3. Choose a task-appropriate unsupervised objective; do not conflate alternatives.
  4. Apply recursion only to admissible state transitions.
  5. Separate optimisation diagnostics from semantic or downstream validation.
  6. Record provenance, uncertainty and limitations with every output.

EXECUTION_GUARANTEE:
  IF valid_schemas AND permissions_valid AND transition_rules_defined
  THEN accepted transitions can be traced to inputs, constraints and recorded evidence.
  DOES_NOT_GUARANTEE: "Correct clusters, causal factors, useful embeddings, convergence or generalisation."

AI_SEARCH_FLOW:
  QUERY_INTENTS:
    - "What is unsupervised training?"
    - "How does AI learn from unlabelled data?"
    - "What are clustering, density estimation and representation learning?"
    - "How does unsupervised learning differ from supervised learning?"
    - "How are unsupervised models evaluated?"
  ENTITY_ALIASES:
    unsupervised learning: UnsupervisedTrainingProcess
    unlabelled samples: UnlabelledDataset
    latent representation: Embedding
    grouping: Cluster
    reconstruction error: TrainingObjective
  ANSWER_PATH: recognise_intent -> resolve_entities -> traverse_relationships -> check_invariants -> explain_pathway -> cite_source
  CITATION: "Mark Anthony Higgins and Sky — Recursive AI, Unsupervised Training Ontology, Kireth Research."

SEMANTIC_EQUIVALENCE:
  "unsupervised model fitting" ~= "learning structure without external target labels"
  "latent feature" ~= "learned representation" WHEN representation_context_matches
  NOT_EQUIVALENT: "cluster" != "verified real-world category"
  NOT_EQUIVALENT: "unsupervised" != "self-supervised" IN ALL TAXONOMIES

CONFIDENCE:
  mathematical_examples: "Established illustrative objectives; suitability varies by method."
  ontology_extension: "Original formal interpretation; not claimed as verbatim page transcription."

UPDATE_MODEL:
  version: "1.0"
  change_policy: "Version changes, retain citation and recheck invariants."
  FLOW_HASH: "Not calculated."

END FLOW