Multi-label classification remains a challenging task for high-dimensional data samples and their labels both increase the complexity of training models. In this paper, we propose a Robust Label and Feature Space Co-Learning method, referred to as RLFSCL, for multi-label classification. Different from traditional multi-label classification methods which focus on feature space learning through regression directly between data samples and labels, our proposed method can further learn robust low rank label space from this traditional regression method. Therefore, our RLFSCL can learn better low rank feature and label representations simultaneously in original noisy and high dimensional spaces. Experimental comparison on five benchmark datasets, including Rcv1s5, Cal500, and Corel16k4 shows that the proposed RLFSCL algorithm outperforms state-of-the-art multi-label classification methods. The code of RLFSCL is made available on https://github.com/JingChuanTang/RLFSCL.

The knowledge graph has received a lot of interest in the field of recommender systems as side information because it can address the sparsity and cold start issues associated with collaborative filtering-based recommender systems. However, when incorporating entities from a knowledge graph to represent semantic information, most current KG-based recommendation methods are unaware of the relationships between these users and items. As such, the learned semantic information representation of users and items cannot fully reflect the connectivity between users and items. In this paper, we present the PRKG-DI symmetry model, a Personalized Relationships-based Knowledge Graph for recommender systems with Dual-view Items that explores user-item relatedness by mining associated entities in the KG from user-oriented entity view and item-oriented entity view to augment item semantic information. Specifically, PRKG-DI utilizes a heterogeneous propagation strategy to gather information on higher-order user-item interactions and an attention mechanism to generate the weighted representation of entities. Moreover, PRKG-DI provides a score feature as a filter for individualized relationships to evaluate users’ potential interests. The empirical results demonstrate that our approach significantly outperforms several state-of-the-art baselines by 1.6%, 2.1%, and 0.8% on $AUC$, and 1.8%, 2.3%, and 0.8% on $F1$ when applied to three real-world scenarios for music, movie, and book recommendations, respectively.

In multi-label learning, traditional methods try to directly establish mapping functions between samples and their labels. However, such methods may suffer low classification performance due to inherent noise and incoherent representation of samples. Therefore, considering the correlation between samples and their neighbors, as they may share common feature semantics, a multi-label classification method via feature enhancement from neighborhood samples, referred to as DRNSA is proposed. In this method, we construct a Laplacian graph dynamically, by considering the distance of two samples in a projected low rank subspace. With this neighborhood selection strategy, we then build a multi-label classifier via feature enhancement from neighborhood samples. Different from past works that directly build classifiers from samples and their labels, we build a new enhanced data sample which is weighted by its semantically similar neighborhood samples. Thus, our method can obtain better robust subspace in very high noisy data representations. Experiments conducted on cal500, corel5k, corel16k1 and corel16k4 datasets, show a significant out-performance of our DRNSA method over multi-label classification methods, including MLSF, MLFE, BR, PLST, CSSP, CPLST, and FaIE.

Cross domain adaptation aims to improve the performance of the target domain model by making full use of information rich source domain samples. However, as information becomes richer, the noise also increases. In order to improve the reliability of cross domain adaptation, we propose a novel method based on deep robust low rank correlation. Borrowed from the traditional idea of Canonical Correlation Analysis (CCA), we developed a robust correlation model to maximize the correlation between source and target domains. Also, the low-rank characteristics of cross domain data can effectively reduce the negative influence of noisy data. Furthermore, in order that the cross-domain data can share a unifying clustering structure, we introduced a common Laplacian affinity structure. Then the learned features can be smoothed and aligned to the unifying structure. In this way, we obtain a deep robust low rank correlation model with the help of the unifying clustering structure, which can effectively reduce the influence of noise and improve the performance of cross domain adaptation. Experimental results on three data sets including Office-31, ImageCLEF-DA and Office-Home show that our model significantly outperforms state-of-the-art cross domain adaptation methods.