Laura Balzano
Office: EECS 4114
1301 Beal Ave, Ann Arbor, MI 48109
Phone: (734) 615-9451
Laura Balzano
Office: EECS 4114
1301 Beal Ave, Ann Arbor, MI 48109
Phone: (734) 615-9451
I’m excited that our results for the K-Subspaces algorithm were accepted to ICML. My postdoc Peng Wang will be presenting his excellent work; you may read the paper here or attend his session if you are interested. K-Subspaces (KSS) is a natural generalization of K-Means to higher dimensional centers, originally proposed by Bradley and Mangasarian in 2000. Peng not only showed that KSS converges locally, but that a simple spectral initialization guarantees a close-enough initialization in the case of data drawn randomly from arbitrary subspaces. This makes a giant step in a line of questioning that has been open for more than 20 years. Great work Peng!
Hessam Mahdavifar and I have been awarded funds from the Department of Energy to study sketching in the context of non-real-valued data. Randomized sketching and subsampling algorithms are revolutionizing the data processing pipeline by allowing significant compression of redundant information. However, current research assumes input data are real-valued, when many sensing, storage, and computation modalities in scientific and technological applications are best modeled mathematically as other types of data, including discrete-valued data and ordinal or categorical data, among others. You can read about the project here and read a Q&A here that was highlighted on the DoE office of science website. We are excited about the opportunity to expand in this new direction!
Our work on heteroscedastic PCA continues with our article “HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise,” published in IEEE Transactions on Signal Processing. In this paper we developed novel ascent algorithms to maximize the heteroscedastic PCA likelihood, simultaneously estimating the principal components and the heteroscedastic noise variances. We show a compelling application to air quality data, where it is common to have data both from sensors that are high-quality EPA instruments and others that are consumer grade. Code for the paper experiments is available at https://gitlab.com/heppcat-group, and the HePPCAT method is available as a registered Julia package. Congratulations to my student Kyle Gilman, former student David Hong, and colleague Jeff Fessler.
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