# Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images
## 2 Related Work  
Clustering is an unsupervised learning task that can train a model without any label. Unsupervised learning is beneficial over supervised learning because it reduces the cost of labeling data. Cluster analysis aims to explore or summarize the characteristics of data. This field has seen much research in recent decades. For example, k-means has a history of 50 years. The clustering technique is used for categorical data [3]. The clustering method is employed in image processing, pattern recognition, and image segmentation [20; 21]. The clustering method is widely used in biotechnology, geography, astronomy, psychology, customer relation management, marketing, time series forecasting [5; 22; 23], and stock management [9]. The choice of clustering methods and algorithms is dependent on both the size of individual instances and the number of instances under consideration. While high-dimensional dataset clustering has always been a challenge for clustering methods [24], because of its high dimensionality, it becomes tedious and requires more general methods to cluster different datasets [25]. Some algorithms, such as the subspace clustering technique, ignore irrelevant features unrelated to clustering tasks in \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Explanation** \\ \hline \(Y\) & Feature representation \\ \(W\) & Input to hidden layer weight \\ \(b\) & Bias \\ \(f_{e}\) & Activation function, _Relu_ \\ \(W^{\prime}\) & Tied weights \\ \(f_{d}\) & Decoding function \\ \(F\) & The dimensionality of deep CNN feature \\ \(x\) & Original input \\ \(\tilde{x}\) & Noise input \\ \(z\) & Reconstructed input \\ \(\mu_{j}\) & Cluster centroid \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations and explanations. high-dimensional data spaces. [26; 27]. As with the general subspace clustering methods, they are generally not applicable to combinations of arbitrary features. Projection clustering is another way to assign each point to a single cluster, but clustering may occur in different subspaces. This algorithm uses a special distance function when performing regular clustering tasks [24] In some cases, due to the overlap, it should be noted that not all algorithms can discover a distinctive cluster for each point nor the result between all clusters in sub-spaces involved in a high-dimensional space with feature vector commonalities between attributes. Correlations between features or sub-set of features lead to distinct spatial shapes of clusters. Therefore, local patterns are used to identify the differences between clustering items. Biclustering can be regarded as correlation clustering since both are strongly linked. Biclustering identifies clusters of objects based on shared attributes [28]. Due to the discovery of novel methods, interest in this deep architecture has been revived, using deep auto-encoders to convert high-dimensional data into low-dimensional representations. Recent research proved successful at learning their parameters. There is still no explicit knowledge of what constitutes useful representations to initialize deep-stacked architectures or what specific unsupervised criteria can best guide their learning. However, some algorithms perform well, such as semi-supervised embedding, auto-encoders, restricted Boltzmann machines [29], and kernel PCA [30]. There are, widely, two categories of deep clustering algorithms. (i) Learning latent space representation of data separately and performing clustering tasks directly on a compressed data representation. (ii) The second stage of work applies dimension reduction and clustering tasks simultaneously [31; 32]. Existing unsupervised deep learning frameworks and methods directly benefit the first category of algorithms. For example, using deep-stacked auto-encoder to learn compressed representation and perform k-means for clustering tasks. While the second category explicitly assigns a clustering loss, faking clustering error in unsupervised deep learning and optimizing it iteratively [16; 17; 33; 34].