Fechnerian Scaling: Dissimilarity Cumulation Theory
This is a chapter for the third volume of the New Handbook of Mathematical Psychology. It presented mathematical foundations of Fechnerian Scaling, a method of metrizing stimulus spaces based on subjective measures of dissimilarity.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Quality-diversity in dissimilarity spaces
The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-diversity algorithms in generic dissimilarity spaces. In particular, we ins…
DiversityContinuous Multidimensional Scaling
Multidimensional scaling (MDS) is the act of embedding proximity information about a set of $n$ objects in $d$-dimensional Euclidean space. As originally conceived by the psychometric community, MDS was concerned with em…
A Unified Framework for Critical Scaling of Inverse Temperature in Self-Attention
Length-dependent logit rescaling is widely used to stabilize long-context self-attention, but existing analyses and methods suggest conflicting inverse-temperature laws for the context length $n$, ranging from $(\log n)^…
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond
The FedProx algorithm is a simple yet powerful distributed proximal point optimization method widely used for federated learning (FL) over heterogeneous data. Despite its popularity and remarkable success witnessed in pr…
Federated LearningNeuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms
We introduce Non-Euclidean-MDS (Neuc-MDS), an extension of classical Multidimensional Scaling (MDS) that accommodates non-Euclidean and non-metric inputs. The main idea is to generalize the standard inner product to symm…
Dimensionality Reduction