Vectorbased

Vectorbased is an adjective used to describe systems, representations, or processes that rely on vectors or vector spaces to encode information. In general, vectorbased approaches use ordered sets of numbers—vectors—to capture properties, positions, features, or relations within a mathematical space. They emphasize linear algebra operations such as scaling, rotation, projection, and similarity measurement.

The term spans multiple disciplines. In computer graphics, vector-based graphics encode shapes as mathematical paths rather than pixel grids, enabling scalable rendering (examples include SVG, PostScript). In GIS, vector data stores features as points, lines, and polygons, as opposed to raster grids. In natural language processing and data science, many representations are vector-based, with word, sentence, or entity embeddings that place semantic information in high-dimensional spaces; similarity is computed via measures like cosine similarity or dot product. In information retrieval, vector databases index high-dimensional vectors to enable nearest-neighbor search.

Advantages of vectorbased approaches include resolution independence, compact storage for sparse features, and efficient geometric transformations. They support operations such as transformation, rotation, scaling, and algebraic manipulation. Limitations include the curse of dimensionality, interpretability challenges, and storage or computation costs for very large vectors and datasets. Quality depends on the quality of the representation and the learning objective used to derive the vectors.

Notable examples and related concepts include SVG and vector fonts, raster vs vector graphics, vector space models, and embedding methods such as word2vec, GloVe, and contextual embeddings. Vector databases, such as Faiss or Pinecone, are specialized indexes designed for high-dimensional vectors.