This paper attempts to provide an up-to-date depiction and analysis of the consumer’s attitude towards sustainability of fast fashion products in the UK. Four related strands of literature are reviewed to establish a tri-component model of attitude (ABC), i.e., Affective, Behavioural and Cognitive. A wide set of determinants for attitude is identified, including income, price, gender, culture, religion, age, etc. Based on this conceptual framework, an online questionnaire is designed and sent to university students and alumni in the UK, returning 128 valid responses. Both descriptive statistics and regression analysis (oprobit) are employed to shed light on the three components of attitude towards sustainability. It is found that cognitive and behavioural components converge across cultures and religions, but the affective component remains significantly diverse. Employment status contributes to the awareness, decision and feeling of sustainability features, but gender only matters for purchase decisions. In general, there is an improved cognitive and affective awareness of sustainability, but this does not automatically translate to purchase behaviour. Policy interventions like taxes and subsidies are still needed to foster sustainability in the fast fashion industry.

Feature selection (FS), which identifies the relevant features in a data set to facilitate subsequent data analysis, is a fundamental problem in machine learning and has been widely studied in recent years. Most FS methods rank the features in order of their scores based on a specific criterion and then select the k top-ranked features, where k is the number of desired features. However, these features are usually not the top-k features and may present a suboptimal choice. To address this issue, we propose a novel FS framework in this article to select the exact top-k features in the unsupervised, semisupervised, and supervised scenarios. The new framework utilizes the ℓ0,2-norm as the matrix sparsity constraint rather than its relaxations, such as the ℓ1,2-norm. Since the ℓ0,2-norm constrained problem is difficult to solve, we transform the discrete ℓ0,2-norm-based constraint into an equivalent 0-1 integer constraint and replace the 0-1 integer constraint with two continuous constraints. The obtained top-k FS framework with two continuous constraints is theoretically equivalent to the ℓ0,2-norm constrained problem and can be optimized by the alternating direction method of multipliers (ADMM). Unsupervised and semisupervised FS methods are developed based on the proposed framework, and extensive experiments on real-world data sets are conducted to demonstrate the effectiveness of the proposed FS framework.

A clustering ensemble provides an elegant framework to learn a consensus result from multiple prespecified clustering partitions. Though conventional clustering ensemble methods achieve promising performance in various applications, we observe that they may usually be misled by some unreliable instances due to the absence of labels. To tackle this issue, we propose a novel active clustering ensemble method, which selects the uncertain or unreliable data for querying the annotations in the process of the ensemble. To fulfill this idea, we seamlessly integrate the active clustering ensemble method into a self-paced learning framework, leading to a novel self-paced active clustering ensemble (SPACE) method. The proposed SPACE can jointly select unreliable data to label via automatically evaluating their difficulty and applying easy data to ensemble the clusterings. In this way, these two tasks can be boosted by each other, with the aim to achieve better clustering performance. The experimental results on benchmark datasets demonstrate the significant effectiveness of our method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/space.zip.

Consensus clustering, which learns a consensus clustering result from multiple weak base results, has been widely studied. However, conventional consensus clustering methods only focus on the ensemble process while ignoring the quality improvement of the base results, and thus they just use the fixed base results for consensus learning. In this paper, we provide an alternative idea to improve the final consensus clustering performance by considering the base results refining. In our framework, we adaptively refine the base results in the process of the ensemble. In more detail, on one hand, we ensemble multiple K-means results to learn the consensus one by considering the consensus and diversity; on the other hand, we apply the consensus result to design a graph filter to learn a more cluster-friendly embedding for refining the base K-means results. In our framework, the consensus learning and base results refining are integrated into one unified objective function so that these two tasks can be boosted by each other. Then we design an effective iterative algorithm to optimize the carefully designed objective function. The extensive experiments on benchmark data sets demonstrate that the proposed method can outperform both the single clustering and the state-of-the-art consensus clustering methods. The codes of this paper are released in http://Doctor-Nobody.github.io/codes/ACMK.zip .