Glossario IA
Il dizionario completo dell'Intelligenza Artificiale
Matrix Factorization with Regularization Constraints
Matrix decomposition technique incorporating penalty terms to control model complexity and prevent overfitting by imposing constraints on latent factors.
Semantically-Guided Matrix Factorization
Factorization approach where semantic constraints from external knowledge such as ontologies or lexical embeddings are incorporated to align latent factors with domain concepts.
Spatio-Temporal Matrix Factorization
Matrix decomposition method that simultaneously integrates spatial and temporal constraints to capture the dynamics of data evolving in space and time, such as geolocated time series.
Matrix Factorization with Non-Negativity Constraints (NMF)
Factorization algorithm constraining factor matrices to contain only positive elements, enabling additive interpretation of components, useful in image and text processing.
Matrix Factorization with Sparsity Constraints
Technique imposing sparse structure on factor matrices, promoting selection of relevant features and improving model interpretability in high-dimensional data.
Matrix Factorization with Temporal Smoothing Constraints
Approach integrating constraints to ensure temporal consistency of latent factors between successive time steps, reducing noise and capturing evolutionary trends in time-series data.
Matrix Factorization with Spatial Coherence Constraints
Method that imposes similar latent factors for spatially close entities, exploiting spatial autocorrelation to improve prediction in georeferenced data.
Matrix Factorization with Graph Constraints
Decomposition technique where relationships between entities, modeled by a graph, are used as constraints to regularize latent factors, preserving neighborhood structure in the latent space.
Tensor Matrix Factorization with Constraints
Extension of matrix factorization to tensors (multi-dimensional arrays) where specific constraints for each mode (dimension) are applied to capture complex structures in multi-axis data.
Matrix Factorization with Orthogonality Constraints
Method imposing orthogonality between latent factor vectors, ensuring independence of extracted components and facilitating interpretation, similar to Principal Component Analysis.
Matrix Factorization with Bound Constraints
Approach that limits the values of latent factors within a predefined interval, used to guarantee numerical stability or to respect physical constraints of the modeled problem.
Matrix Factorization with Monotonicity Constraints
Technique imposing a monotonic order relationship on latent factors, essential for modeling phenomena where variables evolve in a predictable manner (e.g., growth, decay).
Matrix Factorization with Fixed Rank Constraints
Decomposition algorithm where the rank of factor matrices is predetermined, explicitly controlling the dimensionality of the latent space for better generalization and interpretability.
Matrix Factorization with Diversity Constraints
Method introducing constraints to maximize diversity between latent factors, avoiding redundancy and promoting the discovery of multiple distinct patterns in data.
Matrix Factorization with Convexity Constraints
Approach where constraints impose a convex structure to the set of admissible solutions, guaranteeing the existence of a global optimum and facilitating model optimization.
Matrix Factorization with Fairness Constraints
Technique integrating algorithmic constraints to mitigate biases and ensure fair predictions across different demographic groups, a major ethical issue in AI.
Matrix Factorization with Causal Constraints
Advanced method incorporating constraints derived from causal models to ensure that the relationships captured in latent factors respect a known cause-effect structure.