Wavelet-based clustering for time-series trend detection

小波包域中的子空间聚类

Subspace Clustering in Wavelet Packets Domain

本文提出小波包域子空间聚类方法，通过多视图低秩MERA张量网络或自停止子带选择提升噪声鲁棒性与可分离性，在多个图像数据集上以线性方法取得媲美甚至超越深度SC算法的性能。

Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering

Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perhaps using wavelet denoising, and then to apply a clustering algorithm. In this paper, we develop a sparse convex wavelet clustering approach that simultaneously denoises and discovers groups. Our approach utilizes convex fusion penalties to achieve agglomeration and group-sparse penalties to denoise through sparsity in the wavelet domain. In contrast to common practice which denoises then clusters, our method is a un…

使用PCA和K-means对非洲发型数据集进行聚类

clustering an african hairstyle dataset using pca and k-means

针对非洲缺乏人脸脸型分类器的问题，本文提出结合Haarcascade特征训练与K-means聚类的非洲女性图像分类方法，助力其适配发型选择。

Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation

Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often plays an important role in the effectiveness of deep clustering, and thus can be a principal cause of performance degradation. In this paper, we propose a clustering-friendly representation learning method using instance discrimination and feature decorrelation. Our deep-learning-based representation learning method is motivated by the properties of classical spectral clustering. Instance discrimination learns similariti…

Soft-Output Signal Detection for Cetacean Vocalizations Using Spectral Entropy, K-Means Clustering and the Continuous Wavelet Transform

Underwater acoustic monitoring systems record many hours of audio data for marine research, making fast and reliable non-causal signal detection paramount. Such detectors assist in reducing the amount of labor required for signal annotations, which often contain large portions devoid of signals. Cetacean vocalization detection based on spectral entropy is investigated as a means of vocalization discovery. Previous techniques using spectral entropy (SE) mostly consider time-frequency enhancement of the entropy measure, and utilize the STFT as its time-frequency (TF) decomposition. SE methods al…

Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram

A framework is proposed for the unconditional generation of synthetic time series based on learning from a single sample in low-data regime case. The framework aims at capturing the distribution of patches in wavelet scalogram of time series using single image generative models and producing realistic wavelet coefficients for the generation of synthetic time series. It is demonstrated that the framework is effective with respect to fidelity and diversity for time series with insignificant to no trends. Also, the performance is more promising for generating samples with the same duration (reshu…