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In a Machine Learning framework, there are three fundamental components that play a crucial role: feature extraction, feature selection, and decision model. In the context of Green Learning, we have Saab transform and Least-squares Normal Transform for feature extraction. Regarding feature selection, we have the Discriminant Feature Test (DFT) and Relevant Feature Test (RFT). However, we do not have a green and interpretable solution for the decision model. For a long time, we applied gradient-boosted trees such as XGBoost or LightGBM as the classifier. Yet, it is known that XGBoost or LightGBM models sacrifice interpretability for performance. Also, the large model size of XGBoost or LightGBM is becoming a huge burden for Green Learning. Therefore, we are motivated to develop a green and interpretable classifier called SLMBoost. The idea is to train a boosting model with the Subspace Learning Machine (SLM). 

Let’s start by looking at a single SLM. In each SLM, we will first identify a discriminant subspace by a series of feature selection techniques, including DFT, RFT, removing correlated features, etc. Then, each SLM will learn a linear regression model on the selected subspace. Figure 1 illustrates a single SLM. To sum up, a single SLM is a linear least square model that operates on a subspace.

Further, we ensemble SLMs in a boosting fashion, which involves training a sequence of SLMs. In this approach, an SLM is trained to correct the errors made by the previous SLMs. To achieve this, the training target of an SLM is the residual of the accumulated result from all the SLMs before it. Two key factors to make this boosting work are changing different features and data subsets. By using DFT/RFT, we can zoom in on different [...]

Congratulations to Yifan Wang for passing his defense today. Yifan’s thesis is titled “Advanced Techniques for Green Image Coding via Hierarchical Vector Quantization.” His Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Aiichiro Nakano (Outside Member). The Committee members appreciated Yifan’s contributions to this field in solving a very challenging problem. MCL News team invited Yifan for a short talk on his thesis and PhD experience, and here is the summary. We thank Yifan for his kind sharing, and wish him all the best in the next journey. A high-level abstract of Yifan’s thesis is given below:

”We developed an image compression codec, “Green Image Codec,” which differs from the traditional intra-prediction/transform-based or deep learning-based codec. We aim to have a codec with low decoding complexity and reasonable performance. We utilize the multi-grid representation as the foundation for our vector quantization-based codec. We proposed several coding tools that were designed and specialized for VQ to improve the rate-distortion gain and reduce the coding complexity. Systemic RDO, which was missing from the traditional VQ-based codec, was added to our framework and relieved the burden of finding suitable coding parameters. We can perform better at a low bit rate compared to H.264 intra with similar complexity.”

Yifan shared his Ph.D. experience at MCL as follows :

“During my time at MCL, I am very grateful for Professor Kuo’s push, hard work, and wisdom; I learned a lot from him. Even though the Ph.D. life for my first year was struggling, the new research topic for VQ-based image coding with few previous references brought a lot of trouble. We made many trials and errors, and I almost lost confidence and motivation because of the difficulty of developing a new codec. However, Professor Kuo kept working hard and provided [...]

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. Programmed Death-Ligand 1 (PD-L1) is an important prognostic marker for ccRCC and CT radiomics can be a non-invasive modality to predict PD-L1.

In a previous study by Shieh et al. [1], the research team explored using radiomic features from computer tomography (CT) imaging to predict the TIME measurements via mIHC analysis. Tumor specimens were categorized as positive or negative, guided by varying thresholds of PD-L1 expression of the tumor epithelium. A total of 1,708 radiomic features were extracted from CT images using a custom-built radiomics pipeline. To predict tumor binary classification based on these radiomic features.

Building upon this work [1], our study explored a novel approach known as Green Learning (GL).

As shown in Fig. 1, our study employs a modularized solution that includes the following modules: the DFT module for feature dimension reduction and the LNT module for new feature generation. Fig [2] presents the sorted DFT loss curve. The x-axis represents the sorted dimension index, while the y-axis represents the corresponding DFT loss value. The colored points scattered on the curve denote the DFT loss of the top 10 variables of importance (VOI), which are identified from the prior study [1]. All these top 10 VOIs possess relatively low DFT loss. The DFT loss value of LNT feature is the lowest among the raw features.

We observed a significant improvement in the radiomics prediction performance of tumor epithelium PD-L1 > 1%, >5% and >10%. Compared to prior research, the AUROC values improved from 0.61 to 0.76, 0.75 to 0.85 and 0.85 to 0.88, respectively.

[1] A. T.-W. Shieh, S. Y. Cen, B. Varghese, D. H. Hwang, X. Lei, K. Gurumurthy, I. Siddiqi, [...]

In 2023, MCL has seen tremendous growth and success. With the departure of esteemed members who have graduated, their impactful research leaves a lasting legacy as they embark on new journeys. Simultaneously, new talent has joined our MCL family, eagerly immersing themselves in research endeavors.

Throughout this remarkable year, MCL members have dedicated themselves tirelessly to their research pursuits, resulting in exceptional publications in esteemed journals and conferences. We express our deepest gratitude for the unwavering commitment and commendable contributions of each member, transforming possibilities into remarkable achievements.

As we approach the festive season, filled with joy and camaraderie, we extend heartfelt Christmas wishes to all MCL members. May this holiday season bring warmth, happiness, and a sense of togetherness to each of you. Let’s not only celebrate the accomplishments of the past year but also cherish the unity and collaborative spirit that define our MCL family. Merry Christmas to all!

Image 1: https://pixabay.com/vectors/merry-christmas-holiday-pattern-6826522/

Image 2: https://pixabay.com/illustrations/christmas-tree-merry-christmas-card-1899904/

In the ever-evolving realm of artificial intelligence (AI) and machine learning (ML), deep learning (DL) has reigned supreme for the past decade. However, its enigmatic nature and computational intricacies have prompted a quest for alternatives. Enter green learning (GL), a novel approach committed to constructing AI systems that are not just powerful but also interpretable, reliable, and sustainable.
GL is structured around three key modules: unsupervised representation learning, supervised feature learning, and supervised decision learning. Our primary focus is on the second module, which tackles the shortcomings of DL.

In the initial stages of GL, a diverse set of representations is crafted without any guiding supervision. These representations then undergo a discriminant feature test (DFT) in the subsequent module, where they are ranked based on their ability to discriminate. The selected discriminant representations become features. While the unsupervised nature of the first module might make GL representations seem less competitive than their DL counterparts, a remedy is proposed.

To address this, a novel approach emerges—creating new features through linear combinations of selected features. This method shows promise in obtaining more discriminant features, introducing a challenge of finding optimal weights for these combinations. Previous search algorithms like probabilistic search, adaptive particle swarm optimization (APSO) search, and stochastic gradient descent (SGD) search have been explored, but they still come with computational expenses.

Enter the least-squares normal transform (LNT), a game-changer. LNT is a supervised method designed to efficiently generate discriminant complementary features. These new features, termed complementary features, complement the original input features, referred to as raw features. The significance of this work lies in two key contributions: the introduction of LNT as an efficient tool for generating discriminant complementary features and its practical application, showcasing its prowess in [...]