MCL Research on Transfer Learning

Transfer learning is an approach to extract features from source domain and transfer the knowledge of source domain to the target domain, so as to improve the performance of target learners while relieving the pressure to collect a lot of target-domain data[1]. It has been put into wide applications, such as image classification and text classification.

Domain adaptation on digital number datasets, namely MNIST, USPS and SVHN, is the task of transferring learning models across different data sets, aiming to conduct cross-domain feature alignment and build a generalized model that is able to predict labels among various digital number datasets.
Presently, we have trained a green-learning based transfer learning model between MNIST and USPS. The first step is preprocessing and feature extraction, including feature processing for different dataset in order to make them visually similar, raw Saab feature extraction [2] and LNT feature transformation [3] followed by cosine similarity check to find discriminant features. The second step is joint subspace generation, in which for each label in source domain, k-means with number of clusters as 1, 2, 4, 8 are performed separately in order to generate 10, 20, 40 and 80 subspaces, then assign target features to generated subspaces. The third step is to utilize assigned datas to conduct weakly supervised learning to predict label
for rest target data samples. Our goal is to compare and analyze the performance of our green-learning based transfer learning models with other models. In future, we aim to conduct transfer learning among these three digital number datasets mutually and improve the accuracy by improving cross-domain feature alignment.

[1] Zhuang, Fuzhen, et al. “A comprehensive survey on transfer learning.” Proceedings of the IEEE 109.1 (2020): 43-76.
[2] Y. Chen, M. Rouhsedaghat, [...]

By |February 25th, 2024|News|Comments Off on MCL Research on Transfer Learning|

MCL Research on Image Demosaicing

In the world of digital images, turning raw sensor data into colorful pictures that we can see is a complex process. Demosaicing, a crucial step in this process, helps convert data from sensors into full-color images. It all started with Bayer color filter arrays, named after Bryce Bayer, which are grids of tiny sensors, each covered by a color filter—usually red, green, or blue.

But making this conversion isn’t easy. Real-world challenges like sensor noise and blurry motion can mess up the final image. And getting accurate data for training computers to do this job is time-consuming.

Because processing images can be slow, especially on devices like phones, we’re looking into simpler methods that still give good results. Recently we’re experimenting with a new regressor named “LQBoost”

LQBoost operates by conducting least-square regressions in successive iterations, gradually narrowing down the gap between its predictions and the actual targets. By focusing on minimizing the residuals—differences between predicted and actual values—LQBoost iteratively enhances its accuracy. Additionally, it employs a thresholding process to prune samples with residuals approaching zero, streamlining the regression process.

Taking LQBoost a step further, we integrate Local Neighborhood Transformation (LNT) to enrich the feature set and capture intricate data structures more effectively. This integration allows for a more nuanced understanding of the data, leading to improved predictions.

Before applying LQBoost to our demosaicing task, we perform a crucial preprocessing step. By clustering the dataset and utilizing cluster purity, we initialize the regression process effectively. This step ensures that each cluster receives an accurate initial prediction, setting the stage for LQBoost to refine these predictions through iterative regression.

Our goal is to create a demosaicing model that’s both accurate and fast. We’ve tested it thoroughly using standard image datasets, making [...]

By |February 18th, 2024|News|Comments Off on MCL Research on Image Demosaicing|

MCL Research on LQBoost Regressor

LQBoost operates on the principle of leveraging successive linear regressions to mimic the target regression. Each least-square regression serves as a simulation of the corresponding target regression space, mapping the feature space into the current target space and approximating samples within it. The residue of the current target space becomes the regression target for the next iteration. Iteratively, samples with residuals nearing zero are pruned through a thresholding process. Building upon the foundation of LQBoost, in addition to iteratively optimizing the model through thresholding, we can further enhance the feature set using LNT. In each iteration, by removing samples with residuals close to zero, we obtain a clearer and more accurate approximation of the target space. Subsequently, to enrich the feature set and better capture the complex structures within the data, we can utilize LNT to perform local features transform. This iterative regression reduces the gap between the cumulative simulation and the target, resulting in increasingly accurate approximations.

Before this, we performed preprocessing by clustering the dataset into several clusters and using the purity of each cluster as the initial value for regression. If some clusters have high purity, the samples within those clusters use the major label of the cluster as their predicted value. For other clusters, we use the purity as the initial predicted value, and the residue generated by these clusters serves as the target space for the first layer of least square regression in LQBoost.
This preprocessing step is essential for initializing the regression process effectively. By clustering the dataset and utilizing cluster purity, we can assign more accurate initial predictions for each cluster. High-purity clusters, where most samples belong to a single class, provide a straightforward prediction based on the majority label. On [...]

By |February 11th, 2024|News|Comments Off on MCL Research on LQBoost Regressor|

MCL Research on SLMBoost Classifier

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 [...]

By |February 4th, 2024|News|Comments Off on MCL Research on SLMBoost Classifier|

Congratulations to Xuejing Lei for Passing Her Defense

Congratulations to Xuejing Lei for passing her defense. Xuejing’s thesis is titled “Green Image Generation and Label Transfer Techniques.” Her Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Aiichiro Nakano (Outside Member). The Committee members praised Xuejing’s novel contributions and her excellent presentation. Many thanks to our lab members for participating in her rehearsal and providing valuable feedback. MCL News team invited Xuejing for a short talk on her thesis and PhD experience, and here is the summary. We thank Xuejing for her kind sharing, and wish her all the best in the next journey. A high-level abstract of Xuejing’s thesis is given below:

”We designed several generative modeling solutions for texture synthesis, image generation, and image label transfer. Unlike deep-learning-based methods, Our developed generative methods address small model sizes, mathematical transparency, and efficiency in training and generation. We first presented an efficient and lightweight solution for texture synthesis, which can generate diverse texture images given one exemplary texture image. Then, we proposed a novel generative pipeline named GenHop and reformulated it to improve its efficiency and model sizes, yielding our final Green Image generation method. To demonstrate the generalization ability of our generative modeling concepts, we finally adapt it to an image label transfer task and propose a method called Green Image Label Transfer for unsupervised domain adaptation. ”

Xuejing shared her Ph.D. experience at MCL as follows :

I would like first to express my gratitude to Prof. Kuo for his guidance, patience, and unwavering support throughout this journey. His attitude and passion for research have been invaluable to my growth and success. We have been exploring new directions in this field. Since there were few works we could refer to, I experienced a difficult time when Prof. Kuo asked me to tangle [...]

By |January 28th, 2024|News|Comments Off on Congratulations to Xuejing Lei for Passing Her Defense|

Congratulations to Yifan Wang for Passing His Defense

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 [...]

By |January 21st, 2024|News|Comments Off on Congratulations to Yifan Wang for Passing His Defense|

Welcome to Join MCL as an Intern Sanket Kumbhar

We are so happy to welcome a new graduate member,  Sanket Kumbhar, joining MCL as an intern. Here is a quick interview with Sanket:
1. Could you briefly introduce yourself and your research interests?

Hi, my name is Sanket Rajendra Kumbhar. I am a graduate student at USC, pursuing my Masters in Electrical Engineering. I completed my undergraduate studies in India and have two years of professional experience in Electrical Hardware. My primary research interests lie in Signal and Image processing, Machine Learning.
2. What is your impression about MCL and USC?
USC is renowned in various fields, including diverse research areas and athletic programs. It offers numerous opportunities for individuals to advance in their careers. MCL is one such place, where students are nurtured, trained, and equipped to achieve their career aspirations. The members at MCL are supportive, focused, and enthusiastic. The lab’s director, Professor Jay Kuo, inspires everyone with his hard work and passion for his work.
 3. What is your future expectation and plan in MCL?
For the Spring 2024 semester I will be working on the Green Learning approach for processing of multivariate time series with missing values. I am looking forward to continuing my contribution to ongoing research in Green Learning at MCL in the future.

By |January 14th, 2024|News|Comments Off on Welcome to Join MCL as an Intern Sanket Kumbhar|
  • Permalink Gallery

    MCL Research on PD-L1 Prediction in Clear Cell Renal Cell Carcinoma

MCL Research on PD-L1 Prediction in Clear Cell Renal Cell Carcinoma

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, [...]

By |January 7th, 2024|News|Comments Off on MCL Research on PD-L1 Prediction in Clear Cell Renal Cell Carcinoma|

Happy New Year!

As we step into 2024, we want to wish all MCL members an amazing year ahead. May this year bring you lots of happiness, success, and the strength to chase your dreams fearlessly. Here’s to a fantastic year of growth and exciting opportunities for everyone in the MCL community!


Image credit: numbers_67186845.htm#query=happy%20new%20year%202024&position=0&from_view=keyword&track=ais&uuid=776dff76-63c8-4a54-b5a7-275aff3f762e

By |December 30th, 2023|News|Comments Off on Happy New Year!|

Merry Christmas

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:

Image 2:

By |December 17th, 2023|News|Comments Off on Merry Christmas|