Matrix Co-completion for Multi-label Classification with Missing Features and Labels

05/23/2018
by   Miao Xu, et al.
0

We consider a challenging multi-label classification problem where both feature matrix and label matrix have missing entries. An existing method concatenated and as [; ] and applied a matrix completion (MC) method to fill the missing entries, under the assumption that [; ] is of low-rank. However, since entries of take binary values in the multi-label setting, it is unlikely that is of low-rank. Moreover, such assumption implies a linear relationship between and which may not hold in practice. In this paper, we consider a latent matrix that produces the probability σ(Z_ij) of generating label Y_ij, where σ(·) is nonlinear. Considering label correlation, we assume [; ] is of low-rank, and propose an MC algorithm based on subgradient descent named co-completion (COCO) motivated by elastic net and one-bit MC. We give a theoretical bound on the recovery effect of COCO and demonstrate its practical usefulness through experiments.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/13/2018

Clipped Matrix Completion: a Remedy for Ceiling Effects

We consider the recovery of a low-rank matrix from its clipped observati...
research
11/17/2014

Errata: Distant Supervision for Relation Extraction with Matrix Completion

The essence of distantly supervised relation extraction is that it is an...
research
04/08/2019

Multi-View Matrix Completion for Multi-Label Image Classification

There is growing interest in multi-label image classification due to its...
research
05/19/2018

Transduction with Matrix Completion Using Smoothed Rank Function

In this paper, we propose two new algorithms for transduction with Matri...
research
03/17/2017

Nonconvex One-bit Single-label Multi-label Learning

We study an extreme scenario in multi-label learning where each training...
research
06/25/2015

Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis

Subspace recovery from corrupted and missing data is crucial for various...
research
07/23/2019

Collaborative Filtering and Multi-Label Classification with Matrix Factorization

Machine learning techniques for Recommendation System (RS) and Classific...

Please sign up or login with your details

Forgot password? Click here to reset