Nonnegative matrix factorization (NMF), a well-known technique to find parts-based representations of nonnegative data, has been widely studied.

In this paper, we have explored a new stream of semi- supervised NMF and proposed a novel ranking preserving nonnegative matrix factorization (RPNMF).

Nonnegative matrix factorization (NMF), a well-known technique to find parts-based representations of nonnegative data, has been widely studied.

Nonnegative matrix factorization (NMF), a well-known technique to find parts-based representations of nonnegative data, has been widely studied.

This paper proposes a novel ranking preserving nonnegative matrix factorization (RPNMF) approach, which enforces the learned representations to be ranked ...

Analogues of characterizations of rank-preserving operators on field-valued matrices are determined for matrices witheentries in certain structures contained ...

Nonnegative matrix factorization (NMF), a well-known technique to find parts-based representations of nonnegative data, has been widely studied.

Nonnegative matrix factorization (NMF), a well-known technique to find parts-based representations of nonnegative data, has been widely studied.

Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi : Ranking Preserving Nonnegative Matrix Factorization. IJCAI 2018: 2776-2782.

In this paper, we propose a novel objective which provides enough probabilistic semantics of intrinsic local topology via the probability preserving ...