Transductive learning

Q: How deeply does Optimization Algorithms: AI techniques for design, planning, and control problems cover transductive learning?

Defines the training restriction, scalability drawback, and static-graph fit but does not detail individual algorithms.

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

Transductive embedding is a graph embedding technique that learns representations only for entities observed during training.

Transductive learning learns embeddings only for the nodes present during training. Each representation is tied to that observed graph, so the model cannot directly embed newly added nodes. section 11.3.1

This approach suits static graphs but is difficult to scale. It contrasts with inductive embedding, which learns a function from node features and local structure that can process unseen nodes and evolving graphs. section 11.3.1

Common transductive graph-embedding approaches include:

Random-walk methods such as node2vec and DeepWalk. section 11.3.1
Matrix-factorization methods such as graph factorization and HOPE. section 11.3.1

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

Cover of Optimization Algorithms: AI techniques for design, planning, and control problems

liveBook manning.com

Read Optimization Algorithms: AI techniques for design, planning, and control problems on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Graph Neural Networks in ActionA transductive embedding method directly learns representations for nodes in a fixed graph and does not naturally generalize to unseen nodes or graphs.
liveBook manning.com

Knowledge Graphs and LLMs in ActionTransductive learning learns representations or predictions for the specific nodes and graph structure observed during training rather than a general rule for unseen nodes.

Compare how 3 books explain transductive learning →

Related concepts

inductive inferencecontrasts with · Inductive inference is the process of deriving general rules, hypotheses, mappings, or predictions from observed examples or data.
inductive embeddingalternative to · Inductive embedding is a graph representation technique that learns a function for generating embeddings of previously unseen entities or graphs.
Node2Vecused by · Node2Vec is a graph representation-learning algorithm that learns node embeddings from biased random walks and their context relationships.
embeddingkind of · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
vectorizationkind of · Vectorization is the representation of data or computations as vectors or arrays so they can be processed numerically or collectively.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.