Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery

12/13/2019
by   Xianzhen Li, et al.
0

For subspace recovery, most existing low-rank representation (LRR) models performs in the original space in single-layer mode. As such, the deep hierarchical information cannot be learned, which may result in inaccurate recoveries for complex real data. In this paper, we explore the deep multi-subspace recovery problem by designing a multilayer architecture for latent LRR. Technically, we propose a new Multilayer Collabora-tive Low-Rank Representation Network model termed DeepLRR to discover deep features and deep subspaces. In each layer (>2), DeepLRR bilinearly reconstructs the data matrix by the collabo-rative representation with low-rank coefficients and projection matrices in the previous layer. The bilinear low-rank reconstruc-tion of previous layer is directly fed into the next layer as the input and low-rank dictionary for representation learning, and is further decomposed into a deep principal feature part, a deep salient feature part and a deep sparse error. As such, the coher-ence issue can be also resolved due to the low-rank dictionary, and the robustness against noise can also be enhanced in the feature subspace. To recover the sparse errors in layers accurately, a dynamic growing strategy is used, as the noise level will be-come smaller for the increase of layers. Besides, a neighborhood reconstruction error is also included to encode the locality of deep salient features by deep coefficients adaptively in each layer. Extensive results on public databases show that our DeepLRR outperforms other related models for subspace discovery and clustering.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 5

10/31/2014

Symmetric low-rank representation for subspace clustering

We propose a symmetric low-rank representation (SLRR) method for subspac...
10/24/2016

Laplacian regularized low rank subspace clustering

The problem of fitting a union of subspaces to a collection of data poin...
08/04/2019

Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation

We propose a novel and unsupervised representation learning model, i.e.,...
03/29/2021

Motion Basis Learning for Unsupervised Deep Homography Estimation with Subspace Projection

In this paper, we introduce a new framework for unsupervised deep homogr...
10/14/2010

Robust Recovery of Subspace Structures by Low-Rank Representation

In this work we address the subspace recovery problem. Given a set of da...
09/28/2019

Robust Knowledge Discovery via Low-rank Modeling

It is always an attractive task to discover knowledge for various learni...
12/13/2019

Deep Self-representative Concept Factorization Network for Representation Learning

In this paper, we investigate the unsupervised deep representation learn...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.