4 research outputs found
Multi-view Fuzzy Representation Learning with Rules based Model
Unsupervised multi-view representation learning has been extensively studied
for mining multi-view data. However, some critical challenges remain. On the
one hand, the existing methods cannot explore multi-view data comprehensively
since they usually learn a common representation between views, given that
multi-view data contains both the common information between views and the
specific information within each view. On the other hand, to mine the nonlinear
relationship between data, kernel or neural network methods are commonly used
for multi-view representation learning. However, these methods are lacking in
interpretability. To this end, this paper proposes a new multi-view fuzzy
representation learning method based on the interpretable Takagi-Sugeno-Kang
(TSK) fuzzy system (MVRL_FS). The method realizes multi-view representation
learning from two aspects. First, multi-view data are transformed into a
high-dimensional fuzzy feature space, while the common information between
views and specific information of each view are explored simultaneously.
Second, a new regularization method based on L_(2,1)-norm regression is
proposed to mine the consistency information between views, while the geometric
structure of the data is preserved through the Laplacian graph. Finally,
extensive experiments on many benchmark multi-view datasets are conducted to
validate the superiority of the proposed method.Comment: This work has been accepted by IEEE Transactions on Knowledge and
Data Engineerin
Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces
© 2018 IEEE. Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data