A unified framework for discrete spectral clustering

Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (i.e., first learning continuous labels and then rounding the learned labels into discrete ones), which may lead to severe information loss and performance degradation. In this work, we study how to achieve discrete clustering as well as reliably generalize to unseen data. We propose a unified spectral clustering scheme which jointly learns discrete clustering labels and robust out-ofsample prediction functions. Specifically, we explicitly enforce a discrete transformation on the intermediate continuous labels, which leads to a tractable optimization problem with a discrete solution. Moreover, to further compensate the unreliability of the learned labels, we integrate an adaptive robust module with 2,p loss to learn prediction function for unseen data. Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to existing clustering approaches

Learning Discrete Hashing Towards Efficient Fashion Recommendation

In our daily life, how to match clothing well is always a troublesome problem especially when we are shopping online to select a pair of matched pieces of clothing from tens of thousands available selections. To help common customers overcome selection issues, recent studies in the recommender system area have started to infer the fashion matching results automatically. The traditional fashion recommendation is normally achieved by considering visual similarity of clothing items or/and item co-purchase history from existing shopping transactions. Due to the high complexity of visual features and the lack of historical item purchase records, most of the existing work is unlikely to make an efficient and accurate recommendation. To address the problem, in this paper, we propose a new model called Discrete Supervised Fashion Coordinates Hashing. Its main objective is to learn meaningful yet compact high-level features of clothing items, which are represented as binary hash codes. In detail, this learning process is supervised by a clothing matching matrix, which is initially constructed based on limited known matching pairs and subsequently on the self-augmented ones. The proposed model jointly learns the intrinsic matching patterns from the matching matrix and the binary representations from the clothing items’ images, where the visual feature of each clothing item is discretized into a fixed-length binary vector. The binary representation learning significantly reduces the memory cost and accelerates the recommendation speed. The experiments compared with several state-of-the-art approaches have evidenced the superior performance of the proposed approach on efficient fashion recommendation

Transductive visual-semantic embedding for zero-shot learning

Zero-shot learning (ZSL) aims to bridge the knowledge transfer via available semantic representations (e.g., attributes) between labeled source instances of seen classes and unlabelled target instances of unseen classes. Most existing ZSL approaches achieve this by learning a projection from the visual feature space to the semantic representation space based on the source instances, and directly applying it to the target instances. However, the intrinsic manifold structures residing in both semantic representations and visual features are not effectively incorporated into the learned projection function. Moreover, these methods may suffer from the inherent projection shift problem, due to the disjointness between seen and unseen classes. To overcome these drawbacks, we propose a novel framework termed transductive visualsemantic embedding (TVSE) for ZSL. In specific, TVSE first learns a latent embedding space to incorporate the manifold structures in both labeled source instances and unlabeled target instances under the transductive setting. In the learned space, each instance is viewed as a mixture of seen class scores. TVSE then effectively constructs the relational mapping between seen and unseen classes using the available semantic representations, and applies it to map the seen class scores of the target instances to their predictions of unseen classes. Extensive experiments on four benchmark datasets demonstrate that the proposed TVSE achieves competitive performance compared with the stateof- the-arts for zero-shot recognition and retrieval tasks

Unifying multi-source social media data for personalized travel route planning

Travel route planning aims to mine user's attributes and recom-mend personalized routes. How to build interest model for users and understand their real intention brings great challenges. This paper presents an approachwhich mines the user interest model by multi-source social media (e.g., travelogues and check-in records), and understands the user's real intention by active behavior such as point of interest (POI) inputs. In order to unify heterogeneous data from difierent sources, a topical package is built as the mea-surement space. Based on the topical package, user topical pack-Age is modeled to find user interest and route topical package is constructed to describe the attributes of each route. User's active behavior can also be considered during route planning, where top ranked routes are finally recommended. The proposedmulti-source topical package (MSTP) approach is evaluated on a real dataset and comparedwith two state-of-The-Art methods. The result shows that MSTP performs better for providing personalized travel routes

Robust discrete code modeling for supervised hashing

Recent years have witnessed the promising efficacy and efficiency of hashing (also known as binary code learning) for retrieving nearest neighbor in large-scale data collections. Particularly, with supervision knowledge (e.g., semantic labels), we may further gain considerable performance boost. Nevertheless, most existing supervised hashing schemes suffer from the following limitations: (1) severe quantization error caused by continuous relaxation of binary codes; (2) disturbance of unreliable codes in subsequent hash function learning; and (3) erroneous guidance derived from imprecise and incomplete semantic labels. In this work, we propose a novel supervised hashing approach, termed as Robust Discrete Code Modeling (RDCM), which directly learns high-quality discrete binary codes and hash functions by effectively suppressing the influence of unreliable binary codes and potentially noisily-labeled samples. RDCM employs ℓ norm, which is capable of inducing sample-wise sparsity, to jointly perform code selection and noisy sample identification. Moreover, we preserve the discrete constraint in RDCM to eliminate the quantization error. An efficient algorithm is developed to solve the discrete optimization problem. Extensive experiments conducted on various real-life datasets show the superiority of the proposed RDCM approach as compared to several state-of-the-art hashing methods

Discrete Nonnegative Spectral Clustering

© 1989-2012 IEEE. Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby leading to severe information loss and performance degradation. In this work, we study how to achieve discrete clustering as well as reliably generalize to unseen data. We propose a novel spectral clustering scheme which deeply explores cluster label properties, including discreteness, nonnegativity, and discrimination, as well as learns robust out-of-sample prediction functions. Specifically, we explicitly enforce a discrete transformation on the intermediate continuous labels, which leads to a tractable optimization problem with a discrete solution. Besides, we preserve the natural nonnegative characteristic of the clustering labels to enhance the interpretability of the results. Moreover, to further compensate the unreliability of the learned clustering labels, we integrate an adaptive robust module with ℓ 2,p loss to learn prediction function for grouping unseen data. We also show that the out-of-sample component can inject discriminative knowledge into the learning of cluster labels under certain conditions. Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to several existing clustering approaches

Event Early embedding: Predicting event volume dynamics at early stage

Social media has become one of the most credible sources for delivering messages, breaking news, as well as events. Pre-dicting the future dynamics of an event at a very early stage is signi cantly valuable, e.g, helping company anticipate mar-keting trends before the event becomes mature. However, this prediction is non-Trivial because a) social events always stay with \noise" under the same topic and b) the infor-mation obtained at its early stage is too sparse and limited to support an accurate prediction. In order to overcome these two problems, in this paper, we design an event early embedding model (EEEM) that can 1) extract social events from noise, 2) nd the previous similar events, and 3) predict future dynamics of a new event. Extensive experiments con-ducted on a large-scale dataset of Twitter data demonstrate the capacity of our model on extract events and the promis-ing performance of prediction by considering both volume information as well as content information

Bidirectional discrete matrix factorization hashing for image search

Unsupervised image hashing has recently gained significant momentum due to the scarcity of reliable supervision knowledge, such as class labels and pairwise relationship. Previous unsupervised methods heavily rely on constructing sufficiently large affinity matrix for exploring the geometric structure of data. Nevertheless, due to lack of adequately preserving the intrinsic information of original visual data, satisfactory performance can hardly be achieved. In this article, we propose a novel approach, called bidirectional discrete matrix factorization hashing (BDMFH), which alternates two mutually promoted processes of 1) learning binary codes from data and 2) recovering data from the binary codes. In particular, we design the inverse factorization model, which enforces the learned binary codes inheriting intrinsic structure from the original visual data. Moreover, we develop an efficient discrete optimization algorithm for the proposed BDMFH. Comprehensive experimental results on three large-scale benchmark datasets show that the proposed BDMFH not only significantly outperforms the state-of-the-arts but also provides the satisfactory computational efficiency