Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, ...

May 6, 2015 · In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L 1 -norm learning, which further extends the LLE- ...

Dive into the research topics of 'Approximate Orthogonal Sparse Embedding for Dimensionality Reduction'. Together they form a unique fingerprint. Sort by ...

Feb 13, 2023 · The sparse Johnson-Lindenstrauss transform is one of the central techniques in dimensionality reduc- tion. It supports embedding a set of n ...

Aug 8, 2023 · Spectral Embedding is another non-linear dimensionality reduction technique that also happens to be an unsupervised machine learning algorithm.

Mar 11, 2021 · Results: We present a novel dimensionality reduction method which uses sparse, or- thogonal projections to discover linear separations in ...

We propose an interpretable method featMAP to preserve source features by tangent space embedding. The core of our proposal is to utilize local singular value ...

In this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables.

The Johnson-Lindenstrauss embedding represents a fundamental tool for dimensionality reduction, and is usually realized via random matrices. It was an early ...

Abstract: Orthogonal Tensor Neighborhood Preserving Embedding (OTNPE) is an efficient dimensionality reduc- tion algorithm for two-dimensional images.