DeepAI AI Chat
Log In Sign Up

A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data

by   Chengyu Zhou, et al.

This paper proposes a supervised dimension reduction methodology for tensor data which has two advantages over most image-based prognostic models. First, the model does not require tensor data to be complete which expands its application to incomplete data. Second, it utilizes time-to-failure (TTF) to supervise the extraction of low-dimensional features which makes the extracted features more effective for the subsequent prognostic. Besides, an optimization algorithm is proposed for parameter estimation and closed-form solutions are derived under certain distributions.


page 20

page 25


Gradient-based kernel dimension reduction for supervised learning

This paper proposes a novel kernel approach to linear dimension reductio...

A Closed-Form Solution to Tensor Voting: Theory and Applications

We prove a closed-form solution to tensor voting (CFTV): given a point s...

Adaptive Randomized Dimension Reduction on Massive Data

The scalability of statistical estimators is of increasing importance in...

Mixtures of Matrix Variate Bilinear Factor Analyzers

Over the years data is becoming increasingly higher dimensional, which h...

Zero-inflated Poisson Factor Model with Application to Microbiome Absolute Abundance Data

Dimension reduction of high-dimensional microbiome data facilitates subs...

Tensor Fields for Data Extraction from Chart Images: Bar Charts and Scatter Plots

Charts are an essential part of both graphicacy (graphical literacy), an...

Multilinear Discriminant Analysis using a new family of tensor-tensor products

Multilinear Discriminant Analysis (MDA) is a powerful dimension reductio...