Experimental Analysis of Legendre Decomposition in Machine Learning
In this technical report, we analyze Legendre decomposition for non-negative tensor in theory and application. In theory, the properties of dual parameters and dually flat manifold in Legendre decomposition are reviewed, and the process of tensor projection and parameter updating is analyzed. In app...
Saved in:
Main Authors | , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
12.08.2020
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | In this technical report, we analyze Legendre decomposition for non-negative
tensor in theory and application. In theory, the properties of dual parameters
and dually flat manifold in Legendre decomposition are reviewed, and the
process of tensor projection and parameter updating is analyzed. In
application, a series of verification experiments and clustering experiments
with parameters on submanifold were carried out, hoping to find an effective
lower dimensional representation of the input tensor. The experimental results
show that the parameters on submanifold have no ability to be directly used as
low-rank representations. Combined with analysis, we connect Legendre
decomposition with neural networks and low-rank representation applications,
and put forward some promising prospects. |
---|---|
DOI: | 10.48550/arxiv.2008.05095 |