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Found 9 papers, 3 papers with code
Non-Parametric Estimation of Manifolds from Noisy Data
Assuming that the data was sampled uniformly from a tubular neighborhood of $\mathcal{M}\in \mathcal{C}^k$, a compact manifold without boundary, we present an algorithm that takes a point $r$ from the tubular neighborhood and outputs $\hat p_n\in \mathbb{R}^D$, and $\widehat{T_{\hat p_n}\mathcal{M}}$ an element in the Grassmanian $Gr(d, D)$.
Spectral neighbor joining for reconstruction of latent tree models
A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model.
Spectral Top-Down Recovery of Latent Tree Models
For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps.
Approximation of Functions over Manifolds: A Moving Least-Squares Approach
The resulting approximant is shown to be a function defined over a neighborhood of a manifold, approximating the originally sampled manifold.
Similarity Search Over Graphs Using Localized Spectral Analysis
This paper provides a new similarity detection algorithm.
A max-cut approach to heterogeneity in cryo-electron microscopy
In this paper, we attempt to make the first steps towards rigorous mathematical analysis of the heterogeneity problem in cryo-electron microscopy.
Multi-View Kernel Consensus For Data Analysis
Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set.
PCA-Based Out-of-Sample Extension for Dimensionality Reduction
We prove that the error of the proposed algorithm is bounded.
Probabilistic Robust Autoencoders for Outlier Detection
We first present the Robust AutoEncoder (RAE) objective as a minimization problem for splitting the data into inliers and outliers.
