Sparse Methods for Learning Multiple Subspaces from Large-scale, Corrupted and Imbalanced Data

In many practical applications in machine learning, computer vision, data mining and information retrieval one is confronted with datasets whose intrinsic dimension is much smaller than the dimension of the ambient space. This has given rise to the challenge of effectively learning multiple low-dimensional subspaces from such data. Multi-subspace learning methods based on sparse representation, such as sparse representation based classification (SRC) and sparse subspace clustering (SSC) have become very popular due to their conceptual simplicity and empirical success. However, there have been very limited theoretical explanations for the correctness of such approaches in the literature. Moreover, the applicability of existing algorithms to real world datasets is limited due to their high computational and memory complexity, sensitivity to data corruptions as well as sensitivity to imbalanced data distributions. This thesis attempts to advance our theoretical understanding of sparse representation based multi-subspace learning methods, as well as develop new algorithms for handling large-scale, corrupted and imbalanced data. The first contribution of this thesis is a theoretical analysis of the correctness of such methods. In our geometric and randomized analysis, we answer important theoretical questions such as the effect of subspace arrangement, data distribution, subspace dimension, data sampling density, and so on. The second contribution of this thesis is the development of practical subspace clustering algorithms that are able to deal with large-scale, corrupted and imbalanced datasets. To deal with large-scale data, we study different approaches based on active support and divide-and-conquer ideas, and show that these approaches offer a good tradeoff between high accuracy and low running time. To deal with corrupted data, we construct a Markov chain whose stationary distribution can be used to separate between inliers and outliers. Finally, we propose an efficient exemplar selection and subspace clustering method that outperforms traditional methods on imbalanced data

Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis

The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability to weight the base clusterings without access to the original data and can be affected significantly by the low-quality, or even ill clusterings. Secondly, they generally focus on the instance level or cluster level in the ensemble system and fail to integrate multi-granularity cues into a unified model. To address these two limitations, this paper proposes to solve the clustering ensemble problem via crowd agreement estimation and multi-granularity link analysis. We present the normalized crowd agreement index (NCAI) to evaluate the quality of base clusterings in an unsupervised manner and thus weight the base clusterings in accordance with their clustering validity. To explore the relationship between clusters, the source aware connected triple (SACT) similarity is introduced with regard to their common neighbors and the source reliability. Based on NCAI and multi-granularity information collected among base clusterings, clusters, and data instances, we further propose two novel consensus functions, termed weighted evidence accumulation clustering (WEAC) and graph partitioning with multi-granularity link analysis (GP-MGLA) respectively. The experiments are conducted on eight real-world datasets. The experimental results demonstrate the effectiveness and robustness of the proposed methods.Comment: The MATLAB source code of this work is available at: https://www.researchgate.net/publication/28197031

Diffusion Model with Clustering-based Conditioning for Food Image Generation

Image-based dietary assessment serves as an efficient and accurate solution for recording and analyzing nutrition intake using eating occasion images as input. Deep learning-based techniques are commonly used to perform image analysis such as food classification, segmentation, and portion size estimation, which rely on large amounts of food images with annotations for training. However, such data dependency poses significant barriers to real-world applications, because acquiring a substantial, diverse, and balanced set of food images can be challenging. One potential solution is to use synthetic food images for data augmentation. Although existing work has explored the use of generative adversarial networks (GAN) based structures for generation, the quality of synthetic food images still remains subpar. In addition, while diffusion-based generative models have shown promising results for general image generation tasks, the generation of food images can be challenging due to the substantial intra-class variance. In this paper, we investigate the generation of synthetic food images based on the conditional diffusion model and propose an effective clustering-based training framework, named ClusDiff, for generating high-quality and representative food images. The proposed method is evaluated on the Food-101 dataset and shows improved performance when compared with existing image generation works. We also demonstrate that the synthetic food images generated by ClusDiff can help address the severe class imbalance issue in long-tailed food classification using the VFN-LT dataset.Comment: Accepted for 31st ACM International Conference on Multimedia: 8th International Workshop on Multimedia Assisted Dietary Management (MADiMa 2023