Probabilistic latent semantic analysis

Identity Notes & Concept Scope PLSA denotes the aspect-model probability construction, also called the probabilistic latent semantic indexing model in the LDA comparison. A document can mix several aspects because each co-occurrence has its own latent class. It differs from an SVD reconstruction, a single class for a whole document, and LDA’s prior over document-specific topic weights. The alias PLSI identifies this underlying model, not every cosine, interpolation or model-averaging choice in a retrieval pipeline. Tempered EM is one fitting procedure rather than part of the model’s defining probability law. Compared all 442 pinned AIO labels and scoped synonyms and 418 preceding native IDs, names and aliases. The initial normalized LDA hit denotes LinearDiscriminantAnalysis, whose full Gaussian-classifier record was read; the bare LDA alias is omitted from the new topic-model record. No normalized identity match remains for the final candidate names/aliases. Final full-checkout fixed-string searches, including ignored and hidden files in both worktree and main, return two lines each; both unique contents are existing frontier references. An additional broad LDA search returned 289 worktree and 237 main lines before the preceding batch merged; all 84 distinct contents were read, covering the existing classifier, topic-context references, unrelated programming mnemonics and namespace abbreviations. All 188 semantic label/synonym hits were read. Nineteen exact annotated AIO CLASS nodes and outgoing edges were inspected (18 verified unchanged from prior reading, Biclustering read here), along with their generated records and 16 native neighbors. The 35 contextual YAMLs were read or byte-verified after documented full reading. Absence is bounded to this snapshot, maintained corpus and inspected semantic neighborhood.