High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling

11/07/2021
by   Samudra Herath, et al.
0

The recent rapid growth of the dimension of many datasets means that many approaches to dimension reduction (DR) have gained significant attention. High-performance DR algorithms are required to make data analysis feasible for big and fast data sets. However, many traditional DR techniques are challenged by truly large data sets. In particular multidimensional scaling (MDS) does not scale well. MDS is a popular group of DR techniques because it can perform DR on data where the only input is a dissimilarity function. However, common approaches are at least quadratic in memory and computation and, hence, prohibitive for large-scale data. We propose an out-of-sample embedding (OSE) solution to extend the MDS algorithm for large-scale data utilising the embedding of only a subset of the given data. We present two OSE techniques: the first based on an optimisation approach and the second based on a neural network model. With a minor trade-off in the approximation, the out-of-sample techniques can process large-scale data with reasonable computation and memory requirements. While both methods perform well, the neural network model outperforms the optimisation approach of the OSE solution in terms of efficiency. OSE has the dual benefit that it allows fast DR on streaming datasets as well as static databases.

READ FULL TEXT
research
07/23/2020

Multidimensional Scaling for Big Data

We present a set of algorithms for Multidimensional Scaling (MDS) to be ...
research
04/19/2021

Multidimensional Scaling for Gene Sequence Data with Autoencoders

Multidimensional scaling of gene sequence data has long played a vital r...
research
06/01/2023

Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization

We introduce an efficient and robust auto-tuning framework for hyperpara...
research
07/26/2020

Approaches of large-scale images recognition with more than 50,000 categoris

Though current CV models have been able to achieve high levels of accura...
research
02/24/2022

SQuadMDS: a lean Stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE and UMAP

Multidimensional scaling is a statistical process that aims to embed hig...
research
10/03/2020

Perplexity-free Parametric t-SNE

The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a u...
research
01/18/2014

Dr.Fill: Crosswords and an Implemented Solver for Singly Weighted CSPs

We describe Dr.Fill, a program that solves American-style crossword puzz...

Please sign up or login with your details

Forgot password? Click here to reset