# Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images
## 1 Introduction  
Fashion apparel analysts, researchers, and stakeholders are heading up to know how fashion develops and change in the real world. Modeling fashion behavior is challenging because of personal creativity, interest in fashion choices, adoption, social dependence, and moral values. Many retailers use social network data to analyze fashion trends [1, 2]. To a certain extent, artificial intelligence plays a critical role in the fashion business in assessing a huge number of attributes and processing data accurately, quickly, and efficiently. AI algorithms become proficient assistants for human stylists. Due to the development of machine learning, classification and clustering algorithms are widely used to study the diversity of fashion. The most commonly used algorithms for clustering and classification tasks are K-means [3] and the Gauss mixture model [4]. From the perspective of fashion demand, these techniques can accurately classify automotive fashion products, which shows that these algorithms help solve the decision-making problem. Usually, these algorithms are performed in a supervised or semi-supervised way based on their attribute, description, and visual feature. In some cases, attribute segmentation is not practical because some product attributes may not be able to distinguish one product from another. Although in real-world datasets such as fashion, some researchers have worked on product recommendation and retrieval based on non-visual text-based features. For non-visual attributes, we must manually describe the product characteristics based on theirappearance, color, pattern, and material [5; 6; 7; 8]. In such cases, data become noisy because of human error and can also be quite expensive [9]. We mainly utilize these attributes for product retrieval, article mapping, and product clustering. To perform clustering tasks without labeling data manually, it becomes unsupervised to find an equal representation among products based on image features [10]. The clustering algorithms often use distance-based similarity to perform segmentation. This paper uses the Amazon dataset, which contains high-quality product images. We use a CNN to extract visual features so that each image feature has 4096 resultant vectors. In such high dimensional data space, an interesting phenomenon called the curse of dimensionality arises [11]. The ratio among the farthest and nearest points approaches 1 point, i.e., the points become equally distant. The idea of the nearest neighbor algorithm is that closer items are more relevant than farther items. Still, the distance-based similarity becomes inappropriate if all items are uniformly apart. In Figure 1, we assume a dataset containing 20 data items. These points are spatially distributed between 0 and 2 in different dimensions. Figure 1a illustrates that all the items are placed in one dimension, indicating that almost half of the items are placed in a single cube. Figure 1b demonstrates that the items are placed in a two-dimensional data space. By adding another dimension, the item distribution is not so concentrated. There are only 6 points in the green cube space. Figure 1c shows that the items are placed in a three-dimensional data space. From this example, it can be concluded that if we continue to add more dimensions, the data points will get further apart, and the distance-based similarity will become more inefficient. In such a case, the curse of dimensionality gets strange, and clustering items in such a high-dimensional space becomes a huge mistake. Dimension reduction is a crucial technique in machine learning and data science to combat the challenge of high-dimensional data, commonly known as the curse of dimensionality [12; 13; 14; 15]. Most experts convert data from high to low dimensions and perform clustering assignments directly on a compressed version of the original input. Another approach is to perform dimension reduction and clustering assignments simultaneously [16; 17; 18], which is demonstrated better.